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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
maQs92RfZ8 | 2,026 | rejected | Exploring Federated Pruning for Large Language Models | [
"Pengxin Guo",
"Yinong Wang",
"Wei Li",
"Mengting Liu",
"Ming Li",
"Jinkai Zheng",
"Liangqiong Qu"
] | [
"~Pengxin_Guo1",
"~Yinong_Wang1",
"~Wei_Li82",
"~Mengting_Liu6",
"~Ming_Li21",
"~Jinkai_Zheng1",
"~Liangqiong_Qu2"
] | OpenReview API | LLM pruning has emerged as a promising technology for compressing LLMs, enabling their deployment on resource-limited devices. However, current methodologies typically require access to public calibration samples, which can be challenging to obtain in privacy-sensitive domains. To address this issue, we introduce FedPr... | Reject | 4 | [
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"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "The paper introduces FedPrLLM, a federated pruning framework for large la... | https://openreview.net/forum?id=maQs92RfZ8 | 2505.13547 | papers/maQs92RfZ8.pdf | 0f832bd2d83e0aa83cc5bca4f46bcf23535fe22aacba18b90418dccb761e7e30 | 977,965 | openreview | https://github.com/Pengxin-Guo/FedPrLLM | Pengxin-Guo/FedPrLLM | 7e91d12e0e3250a2790e534d15140ba163cf7739 | repos/maQs92RfZ8.zip | e199692b9595ba88bd1c0e513a67d2dcd63eaf197fe09894a93ff2ac59b8fbf3 | 11,259 | 5 | {
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} | 10 | {
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} | false | 2025-05-15T13:33:09 | {
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Q5CLpqbrFM | 2,025 | rejected | Learning Representations of Instruments for Partial Identification of Treatment Effects | [
"Jonas Schweisthal",
"Dennis Frauen",
"Maresa Schröder",
"Konstantin Hess",
"Niki Kilbertus",
"Stefan Feuerriegel"
] | [
"~Jonas_Schweisthal1",
"~Dennis_Frauen1",
"~Maresa_Schröder1",
"~Konstantin_Hess1",
"~Niki_Kilbertus1",
"~Stefan_Feuerriegel1"
] | OpenReview API | Reliable estimation of treatment effects from observational data is important in many disciplines, such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this work, we leverage arbitrary (potentially high-dimensional) instru... | Reject | 4 | [
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"soundness": 3,
"presentation": 4,
"contribution": 3,
"confidence": 3,
"summary": "This paper provides a method for the partial identification of treatment e... | https://openreview.net/forum?id=Q5CLpqbrFM | 2410.08976 | papers/Q5CLpqbrFM.pdf | 78ed1fb7e0c8058d17c09de55c23dba0815eb61958e93f49b3121f194d09c6fe | 2,027,941 | openreview | https://github.com/JSchweisthal/ComplexPartialIdentif | JSchweisthal/ComplexPartialIdentif | 260547060aa0f2b9e71f0179768f8a534bf6fd19 | repos/Q5CLpqbrFM.zip | 833358fb96d33775e0a634fb59489f4a4f0e0163187deb4dc1cc534cce81ddbf | 11,992 | 7 | {
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} | 10 | {
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} | false | 2024-10-11T16:02:21 | {
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qYb0CANLGC | 2,024 | rejected | Auto-Regressive Next-Token Predictors are Universal Learners | [
"eran malach"
] | [
"~eran_malach1"
] | OpenReview API | Large language models display remarkable capabilities in logical and mathematical reasoning, allowing them to solve complex tasks. Interestingly, these abilities emerge in networks trained on the simple task of next-token prediction. In this work, we present a theoretical framework for studying auto-regressive next-tok... | Reject | 3 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "2 fair",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=qYb0CANLGC | 2309.06979 | papers/qYb0CANLGC.pdf | ebe78d35f4d70fbb451c6e62b818867e5bc4cc9c62b6562566f0c26d4f1b2d26 | 363,218 | openreview | https://github.com/emalach/LinearLM | emalach/LinearLM | 11fb93e06b9738a91b3463b7b0b7b63095928827 | repos/qYb0CANLGC.zip | d5957a399d05f42c6edeb12417cb7a03d2c6949a12da9f08796bc5e978751112 | 7,725 | 3 | {
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} | 11 | {
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} | false | 2024-07-29T20:36:18 | {
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LgjKqSjDzr | 2,022 | rejected | SALT : Sharing Attention between Linear layer and Transformer for tabular dataset | [
"Juseong Kim",
"Jinsun Park",
"Giltae Song"
] | [
"~Juseong_Kim2",
"~Jinsun_Park1",
"gsong@pusan.ac.kr"
] | OpenReview API | Handling tabular data with deep learning models is a challenging problem despite their remarkable success in vision and language processing applications. Therefore, many practitioners still rely on classical models such as gradient boosting decision trees (GBDTs) rather than deep networks due to their superior performa... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=LgjKqSjDzr | null | papers/LgjKqSjDzr.pdf | e51405d2a961863fea10f81c16e63b1564adf13a5f116e713f6a9337c8e0c1a2 | 999,515 | openreview | https://github.com/Juseong03/SALT | Juseong03/SALT | 8d95159be073be64d1ba1af1017ae8870a35005a | repos/LgjKqSjDzr.zip | 1f5edbaadaeb7ad970dcf27b46a64c5559d3962ce2206a0c50a4ca9b4ecf0b04 | 9,616 | 4 | {
".py": 4
} | 10 | {
"Python": 35982
} | false | 2021-10-28T02:33:04 | {
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kmBFHJ5pr0o | 2,021 | rejected | Distributed Adversarial Training to Robustify Deep Neural Networks at Scale | [
"Gaoyuan Zhang",
"Songtao Lu",
"Sijia Liu",
"Xiangyi Chen",
"Pin-Yu Chen",
"Lee Martie",
"Lior Horesh",
"Mingyi Hong"
] | [
"~Gaoyuan_Zhang1",
"~Songtao_Lu1",
"~Sijia_Liu1",
"~Xiangyi_Chen1",
"~Pin-Yu_Chen1",
"lee.martie@ibm.com",
"~Lior_Horesh1",
"~Mingyi_Hong1"
] | OpenReview API | Current deep neural networks are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach, known as adversarial training, has been shown to mitigate the negative impact of adversarial attac... | Reject | null | 4 | [
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],
"rating": "5: Marginally below acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"s... | https://openreview.net/forum?id=kmBFHJ5pr0o | 2206.06257 | papers/kmBFHJ5pr0o.pdf | 479976bacaec51c73c97c58763ee6dad612254263f15ecbb62e51c0093883df0 | 647,562 | openreview | https://github.com/dat-2022/dat | dat-2022/dat | 3fac9f60fcf0213b14cc991b3c1a9b2cf26415ba | repos/kmBFHJ5pr0o.zip | ce7430d997a23c756e61aae1bb1a46b4ffbe2e2e9856edc3deb76f58df45d848 | 15,125 | 10 | {
".py": 10
} | 12 | {
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} | false | 2022-07-31T16:33:09 | {
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HylNWkHtvB | 2,020 | rejected | Domain-Independent Dominance of Adaptive Methods | [
"Pedro Savarese",
"David McAllester",
"Sudarshan Babu",
"Michael Maire"
] | [
"savarese@ttic.edu",
"mcallester@ttic.edu",
"sudarshan@ttic.edu",
"mmaire@uchicago.edu"
] | OpenReview API | From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. I... | Reject | null | 3 | [
{
"id": "rklCY7VJ9r",
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],
"rating": "3: Weak Reject",
"confidence": "",
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"soundness": "",
"presentation": "",
"contribution": "",
"correctness": "",
"summary": "",
"rev... | https://openreview.net/forum?id=HylNWkHtvB | 1912.01823 | papers/HylNWkHtvB.pdf | 69ee064c63d05aba1b8e8f60d445c7df6ebbd405434135b70034287016301d43 | 355,911 | openreview | https://github.com/lolemacs/avagrad | lolemacs/avagrad | 343733151f26a1fa8e504079c6d7934a6038a93b | repos/HylNWkHtvB.zip | 4ae059a3b716f559ead8828fa2c4a0006b88eebdacaceb4066dc60c5be20255d | 8,780 | 5 | {
".py": 5
} | 10 | {
"Python": 26004
} | false | 2020-12-15T04:01:22 | {
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} | {
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} |
S1E64jC5tm | 2,019 | rejected | The Forward-Backward Embedding of Directed Graphs | [
"Thomas Bonald",
"Nathan De Lara"
] | [
"thomas.bonald@telecom-paristech.fr",
"nathan.delara@telecom-paristech.fr"
] | OpenReview API | We introduce a novel embedding of directed graphs derived from the singular value decomposition (SVD) of the normalized adjacency matrix. Specifically, we show that, after proper normalization of the singular vectors,
the distances between vectors in the embedding space are proportional to the mean commute times ... | null | Reject | 3 | [
{
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],
"rating": "5: Marginally below acceptance threshold",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
"r... | https://openreview.net/forum?id=S1E64jC5tm | null | papers/S1E64jC5tm.pdf | 8544849615a647b537158912f5832b63bf3b6415d01ddb6c4f9f951d494c8c04 | 316,920 | openreview | https://github.com/tbonald/directed | tbonald/directed | 2d4b979f296bb0750ee1560ac07d70ff68f7c8dc | repos/S1E64jC5tm.zip | da3639ca90e6775705e265ab6256cb2cf997f0b08f0b91f7b7c6fb5455efc695 | 13,245 | 4 | {
".py": 3,
".ipynb": 1
} | 17 | {
"Python": 21945,
"Jupyter Notebook": 17282
} | false | 2018-11-08T09:38:07 | {
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} | {
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} |
S1EwLkW0W | 2,018 | rejected | Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients | [
"Lukas Balles",
"Philipp Hennig"
] | [
"lukas.balles@tuebingen.mpg.de",
"ph@tue.mpg.de"
] | OpenReview API | The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn’t. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of the stochastic gradient, whereas the update magnitude is solely determined ... | Reject | null | 3 | [
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"rating": "4: Ok but not good enough - rejection",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundne... | https://openreview.net/forum?id=S1EwLkW0W | 1705.07774 | papers/S1EwLkW0W.pdf | 2c820b68da0c383b2903af5ebb1dbdd750ebd91c21a5918648a4350b627fe321 | 656,781 | openreview | https://github.com/lballes/msvag | lballes/msvag | d2d467b6a9c6442d781ecb2f1d4b5c5769556363 | repos/S1EwLkW0W.zip | cec7a33afea60c29789a2ee5ae6350da0f16dec1de490ba4dcf2a99486c1f23b | 8,966 | 3 | {
".py": 3
} | 12 | {
"Python": 10920
} | false | 2018-05-11T14:21:18 | {
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JtX6oaaJ2d | 2,026 | rejected | Improving LLM Unlearning Robustness via Random Perturbations | [
"Dang Huu-Tien",
"Hoang Thanh-Tung",
"Anh Tuan Bui",
"Phuong Minh Nguyen",
"Le-Minh Nguyen",
"Naoya Inoue"
] | [
"~Dang_Huu-Tien1",
"~Hoang_Thanh-Tung1",
"~Anh_Tuan_Bui2",
"~Phuong_Minh_Nguyen3",
"~Le-Minh_Nguyen1",
"~Naoya_Inoue1"
] | OpenReview API | Here, we show that current state-of-the-art LLM unlearning methods inherently reduce models' robustness, causing them to misbehave even when a single non-adversarial forget-token is present in the retain-query. Toward understanding underlying causes, we propose a novel theoretical framework that reframes the *unlearnin... | Reject | 4 | [
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"summary": "This work focuses on understanding and improving robustness of llm unlearn... | https://openreview.net/forum?id=JtX6oaaJ2d | 2501.19202 | papers/JtX6oaaJ2d.pdf | 047638bbcecfff54980a5e5c942fd1cddb60e523ef9129df9c8399213c0fa4d7 | 7,340,032 | openreview | https://github.com/RebelsNLU-jaist/llmu-robustness | RebelsNLU-jaist/llmu-robustness | cdda05906725da2f6c1b7d5792eee7d780811541 | repos/JtX6oaaJ2d.zip | 2dbe319e41d72dbf0ff8662f7b10e0361be5d485a9612997ec042818202674a3 | 25,397 | 14 | {
".py": 8,
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} | 16 | {
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} | false | 2026-06-02T05:43:33 | {
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22ywev7zMt | 2,025 | rejected | On the Out-of-Distribution Generalization of Self-Supervised Learning | [
"Wenwen Qiang",
"Jingyao Wang",
"Zeen Song",
"Jiangmeng Li",
"Changwen Zheng"
] | [
"~Wenwen_Qiang1",
"~Jingyao_Wang1",
"~Zeen_Song1",
"~Jiangmeng_Li1",
"~Changwen_Zheng1"
] | OpenReview API | In this paper, we focus on the out-of-distribution (OOD) generalization of self-supervised learning (SSL). By analyzing the mini-batch construction during SSL training phase, we first give one plausible explanation for SSL having OOD generalization. Then, from the perspective of data generation and causal inference, we... | Reject | 3 | [
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"confidence": 4,
"summary": "This paper inspects SSL from a causal perspective, which assumes a SCM for ... | https://openreview.net/forum?id=22ywev7zMt | 2505.16675 | papers/22ywev7zMt.pdf | 80507b1b63d95900be2c420895a12d96d9270abac2fa06b894015b0b6cb03d1f | 2,564,459 | openreview | https://github.com/ML-TASA/PID-SSL | ML-TASA/PID-SSL | 095b2be7fcfe206cbf2a103039a0529d12968846 | repos/22ywev7zMt.zip | fd1087b1fecbe86c4aeab52c00f3c95107d8f1fb6b59b21d37b5b15360c84b76 | 7,332 | 3 | {
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} | 10 | {
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} | false | 2025-06-04T08:40:48 | {
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j8s-BRxXST | 2,023 | rejected | A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration | [
"Shaojie Jiang",
"Ruqing Zhang",
"Svitlana Vakulenko",
"Maarten de Rijke"
] | [
"~Shaojie_Jiang1",
"~Ruqing_Zhang3",
"~Svitlana_Vakulenko1",
"~Maarten_de_Rijke1"
] | OpenReview API | The cross-entropy objective has proved to be an all-purpose training objective for autoregressive language models (LMs). However, without distinguishing problematic tokens, LMs trained using cross-entropy exhibit text degeneration problems. To address this, unlikelihood training has been proposed to reduce the probabil... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=j8s-BRxXST | 2205.02517 | papers/j8s-BRxXST.pdf | 13f30c6527fe9db4674103de3a836b928b2749fd60c3e6b8674717e66380bb95 | 1,753,876 | openreview | https://github.com/ShaojieJiang/CT-Loss | ShaojieJiang/CT-Loss | 21731b590839b31675e55333be289c7b6513ade3 | repos/j8s-BRxXST.zip | 38e85e3862162dbf45eedcd49ca03a36b7201b63f8314f378b6699d19395ea76 | 8,148 | 3 | {
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} | 13 | {
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} | false | 2022-05-11T09:27:35 | {
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xw04RdwI2kS | 2,022 | rejected | Inverse Contextual Bandits: Learning How Behavior Evolves over Time | [
"Alihan Hüyük",
"Daniel Jarrett",
"Mihaela van der Schaar"
] | [
"~Alihan_Hüyük1",
"~Daniel_Jarrett1",
"~Mihaela_van_der_Schaar2"
] | OpenReview API | Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes—such as in healthcare. Though conventional approaches to policy learning almost invariably assume stationarity in behavior, this is hardly true in practice: Medical practice is c... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=xw04RdwI2kS | 2107.06317 | papers/xw04RdwI2kS.pdf | c61e0337f8e7874a6ff8e8d9ee63565d9c735628905549da16b084317611b111 | 2,697,785 | openreview | https://github.com/alihanhyk/invconban | alihanhyk/invconban | b365614697053f3584915ee013cf6fee36e73de5 | repos/xw04RdwI2kS.zip | 8529981d61f2f26816a14f729e68b7d323d31a6e8c4f9518bc435c0dd9d2e4eb | 17,069 | 12 | {
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} | 12 | {
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} | false | 2022-06-07T11:59:01 | {
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EsA9Nr9JHvy | 2,021 | rejected | The Heavy-Tail Phenomenon in SGD | [
"Mert Gurbuzbalaban",
"Umut Simsekli",
"Lingjiong Zhu"
] | [
"~Mert_Gurbuzbalaban1",
"~Umut_Simsekli1",
"~Lingjiong_Zhu1"
] | OpenReview API | In recent years, various notions of capacity and complexity have been proposed for characterizing the generalization properties of stochastic gradient descent (SGD) in deep learning. Some of the popular notions that correlate well with the performance on unseen data are (i) the 'flatness' of the local minimum found by ... | Reject | null | 4 | [
{
"id": "KgWY2lcpI9I",
"reviewer_signature": [
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],
"rating": "7: Good paper, accept",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundness": "",
"... | https://openreview.net/forum?id=EsA9Nr9JHvy | 2006.04740 | papers/EsA9Nr9JHvy.pdf | 2b9da9bfcea610172575c5d32bd6f0bc1e57967da89c7c3f84d5efb39bac18c6 | 718,734 | openreview | https://github.com/umutsimsekli/sgd_ht | umutsimsekli/sgd_ht | 67f2b51055a331f23748bd418c056de1aa7cb48f | repos/EsA9Nr9JHvy.zip | d88873ef01711a7064b73963ad9fb2658772af2e6e4052470c88de222f176ad2 | 12,648 | 6 | {
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} | 12 | {
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} | false | 2022-02-15T13:12:46 | {
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} |
ByxXZpVtPB | 2,020 | rejected | Homogeneous Linear Inequality Constraints for Neural Network Activations | [
"Thomas Frerix",
"Matthias Nießner",
"Daniel Cremers"
] | [
"thomas.frerix@tum.de",
"niessner@tum.de",
"cremers@tum.de"
] | OpenReview API | We propose a method to impose homogeneous linear inequality constraints of the form $Ax\leq 0$ on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time... | Reject | null | 3 | [
{
"id": "SkxudRZP5B",
"reviewer_signature": [
"ICLR.cc/2020/Conference/Paper370/AnonReviewer5"
],
"rating": "3: Weak Reject",
"confidence": "",
"recommendation": "",
"soundness": "",
"presentation": "",
"contribution": "",
"correctness": "",
"summary": "",
"revi... | https://openreview.net/forum?id=ByxXZpVtPB | 1902.01785 | papers/ByxXZpVtPB.pdf | 4677f0bf54d7f1bf6d4fcbdea5d5ea6363aec79cc08e2ac1f2286647ff1ec40c | 733,546 | openreview | https://github.com/tfrerix/constrained-nets | tfrerix/constrained-nets | 1221f3fd2592056c2a00c463d65fe3afa646fd54 | repos/ByxXZpVtPB.zip | 6728797b652ebdf88e23afcf1b96053b5a62108e3c05f38349628efbfd76da9a | 12,562 | 7 | {
".py": 6,
".sh": 1
} | 11 | {
"Python": 29194,
"Shell": 436
} | false | 2021-04-08T19:17:35 | {
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} | {
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} |
rJeZS3RcYm | 2,019 | rejected | Simple Black-box Adversarial Attacks | [
"Chuan Guo",
"Jacob R. Gardner",
"Yurong You",
"Andrew G. Wilson",
"Kilian Q. Weinberger"
] | [
"cg563@cornell.edu",
"jrg365@cornell.edu",
"yy785@cornell.edu",
"andrew@cornell.edu",
"kqw4@cornell.edu"
] | OpenReview API | The construction of adversarial images is a search problem in high dimensions within a small region around a target image. The goal is to find an imperceptibly modified image that is misclassified by a target model. In the black-box setting, only sporadic feedback is provided through occasional model evaluations. In th... | null | Reject | 3 | [
{
"id": "rkxkTpcJTQ",
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"ICLR.cc/2019/Conference/Paper1514/AnonReviewer1"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"p... | https://openreview.net/forum?id=rJeZS3RcYm | 1905.07121 | papers/rJeZS3RcYm.pdf | fcc0747ef59f7d8c34a649fbff3282b4c583e6c42da58706e6596aa4d42b3333 | 7,340,032 | openreview | https://github.com/cg563/simple-blackbox-attack | cg563/simple-blackbox-attack | 3b66937945699f706918277ac55fb171f07d2bcf | repos/rJeZS3RcYm.zip | 617afb689a318d671fbce9c38169653313174229ca1fb5e6fe025f9e86f570e0 | 10,814 | 4 | {
".py": 4
} | 18 | {
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} | false | 2023-03-27T18:29:55 | {
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} | {
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ryykVe-0W | 2,018 | rejected | Learning Independent Features with Adversarial Nets for Non-linear ICA | [
"Philemon Brakel",
"Yoshua Bengio"
] | [
"pbpop3@gmail.com",
"yoshua.bengio@umontreal.ca"
] | OpenReview API | Reliable measures of statistical dependence could potentially be useful tools for learning independent features and performing tasks like source separation using Independent Component Analysis (ICA). Unfortunately, many of such measures, like the mutual information, are hard to estimate and optimize directly. We prop... | Reject | null | 3 | [
{
"id": "ry2lpp_ez",
"reviewer_signature": [
"ICLR.cc/2018/Conference/Paper573/AnonReviewer2"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"pre... | https://openreview.net/forum?id=ryykVe-0W | 1710.05050 | papers/ryykVe-0W.pdf | a7615362a0e07145193cef06d29980c1607eb969c83baca531c847b78bc32a9c | 574,546 | openreview | https://github.com/pbrakel/anica | pbrakel/anica | 79d837addcd98ee9bab301d3966e4919cf9732f3 | repos/ryykVe-0W.zip | a6ba4a1c3a2eb3eceae20e2aa9d3c86c4677f6883b3eb07a93d3175a5e4fd3f2 | 21,667 | 6 | {
".py": 6
} | 16 | {
"Python": 47021
} | false | 2017-10-17T13:02:32 | {
"method": "exact_normalized_title",
"pwc_official": true,
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"pwc_url": "https://paperswithcode.com/paper/learning-independent-features-with"
} | {
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} |
LKv3bx610K | 2,026 | rejected | Mitigating Fine-tuning Risks in LLMs via Safety-Aware Probing Optimization | [
"Chengcan Wu",
"Zhixin Zhang",
"Zeming Wei",
"Yihao Zhang",
"Meng Sun"
] | [
"~Chengcan_Wu1",
"~Zhixin_Zhang5",
"~Zeming_Wei1",
"~Yihao_Zhang8",
"~Meng_Sun1"
] | OpenReview API | The significant progress of large language models (LLMs) has led to remarkable achievements across numerous applications. However, their ability to generate harmful content has sparked substantial safety concerns. Despite the implementation of safety alignment techniques during the pre-training phase, recent research i... | Reject | 4 | [
{
"id": "2XTyVgUrsy",
"reviewer_signature": [
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],
"rating": 4,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper studies the safety alignment problem for LLMs. Specifically, i... | https://openreview.net/forum?id=LKv3bx610K | 2505.16737 | papers/LKv3bx610K.pdf | 6825b5f34b0a8ddf68d8606fdab0ec4a6a579bef466be8f3538d9d7e7d8d0a22 | 4,164,185 | openreview | https://github.com/ChengcanWu/SAP | ChengcanWu/SAP | 487edc3374f24581399040beac6209c388a463ee | repos/LKv3bx610K.zip | 19d021f6b6ea33a4069e1ae54ae35569bd44cedf368b9c92978febc6637e57ed | 12,549 | 5 | {
".py": 5
} | 19 | {
"Python": 19396
} | false | 2026-04-23T15:02:42 | {
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"pwc_url": "https://paperswithcode.com/paper/mitigating-fine-tuning-risks-in-llms-via"
} | {
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} | |
dq3keisMjT | 2,025 | rejected | Phase Transitions in the Output Distribution of Large Language Models | [
"Julian Arnold",
"Flemming Holtorf",
"Frank Schäfer",
"Niels Lörch"
] | [
"~Julian_Arnold2",
"~Flemming_Holtorf1",
"~Frank_Schäfer1",
"~Niels_Lörch1"
] | OpenReview API | In a physical system, changing parameters such as temperature can induce a phase transition: an abrupt change from one state of matter to another. Analogous phenomena have recently been observed in large language models. Typically, the task of identifying phase transitions requires human analysis and some prior underst... | Reject | 4 | [
{
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],
"rating": 3,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper investigates phase transitions in the output distributions of l... | https://openreview.net/forum?id=dq3keisMjT | 2405.17088 | papers/dq3keisMjT.pdf | 0dbb58fa3e3333bffbd88d8ebdaa8aeabedd4e59c972d818efa00c15d9ce6948 | 866,082 | openreview | https://github.com/llmtransitions/llmtransitions | llmtransitions/llmtransitions | 626845657b65205672dd7c01eb2c963b5ebb53c2 | repos/dq3keisMjT.zip | 6d0508b8e098359accc7a34d53da1b4f7df2a075a3852c961b25cf15704b2d5c | 8,937 | 6 | {
".py": 3,
".ipynb": 3
} | 12 | {
"Jupyter Notebook": 14391,
"Python": 8484
} | false | 2025-08-15T13:57:56 | {
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} | {
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} | |
k3VANp85b4S | 2,023 | rejected | On the Robustness of Randomized Ensembles to Adversarial Perturbations | [
"Hassan Dbouk",
"Naresh Shanbhag"
] | [
"~Hassan_Dbouk1",
"~Naresh_Shanbhag1"
] | OpenReview API | Randomized ensemble classifiers (RECs), where one classifier is randomly selected during inference, have emerged as an attractive alternative to traditional ensembling methods for realizing adversarially robust classifiers with limited compute requirements. However, recent works have shown that existing methods for con... | Reject | null | 4 | [
{
"id": "bM79tDbcPZ7",
"reviewer_signature": [
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],
"rating": "",
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=k3VANp85b4S | 2302.01375 | papers/k3VANp85b4S.pdf | fc476d61e8af77d8b69aaa7bcdbf266b7ed3895dcff10aedaa692da3fa5515b2 | 761,683 | openreview | https://github.com/hsndbk4/BARRE | hsndbk4/BARRE | 1a42d89214db58200abd73f29ae5f8ee1e3bebe6 | repos/k3VANp85b4S.zip | 97bb1b39d3c562b65753663e05ab1352faec8e3cff5ec83554c924d0abaa0f6c | 16,547 | 9 | {
".py": 9
} | 15 | {
"Python": 46743
} | false | 2023-09-05T02:14:51 | {
"method": "exact_normalized_title",
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"pwc_url": "https://paperswithcode.com/paper/on-the-robustness-of-randomized-ensembles-to"
} | {
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GIEPR9OomyX | 2,022 | rejected | Langevin Autoencoders for Learning Deep Latent Variable Models | [
"Shohei Taniguchi",
"Yusuke Iwasawa",
"Wataru Kumagai",
"Yutaka Matsuo"
] | [
"~Shohei_Taniguchi1",
"~Yusuke_Iwasawa1",
"~Wataru_Kumagai2",
"~Yutaka_Matsuo1"
] | OpenReview API | Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variable models since it is not scalable to data size owing to its datapoint-wise iterations and slow convergence. This paper proposes the amortiz... | Reject | null | 4 | [
{
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],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=GIEPR9OomyX | 2209.07036 | papers/GIEPR9OomyX.pdf | ec6a4622851af0812767f792117d7a2cf772caadca68c18932f84d15e4f17ad4 | 2,061,206 | openreview | https://github.com/iShohei220/LAE | iShohei220/LAE | d4cfbdb8fdee520fa891610a6afb569dcb9540b6 | repos/GIEPR9OomyX.zip | 60d698fddb4b0817510629baed3f1acbf1cd844130a007b27a8315294a7f879c | 17,968 | 8 | {
".py": 8
} | 13 | {
"Python": 59400
} | false | 2022-09-15T02:16:11 | {
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"pwc_url": "https://paperswithcode.com/paper/langevin-autoencoders-for-learning-deep-1"
} | {
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6VhmvP7XZue | 2,021 | rejected | Open-world Semi-supervised Learning | [
"Kaidi Cao",
"Maria Brbic",
"Jure Leskovec"
] | [
"~Kaidi_Cao1",
"mbrbic@cs.stanford.edu",
"~Jure_Leskovec1"
] | OpenReview API | Supervised and semi-supervised learning methods have been traditionally designed for the closed-world setting which is based on the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, the real world is often open and dynamic, and thus novel previously ... | Reject | null | 4 | [
{
"id": "VVzrM_7HtkB",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper938/AnonReviewer1"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"so... | https://openreview.net/forum?id=6VhmvP7XZue | 2102.03526 | papers/6VhmvP7XZue.pdf | 553138b2d0a9c4b7c990374fbb79dfb52bb5171bea9b220ebc0eab012b88d2c4 | 1,169,175 | openreview | https://github.com/snap-stanford/orca | snap-stanford/orca | 5f33afdeb0aa1ec51d62c9f926f265504ccf9efc | repos/6VhmvP7XZue.zip | ceef60e184d444cf68895244badcf770b14c988fed98df8a714eae641872efd6 | 16,865 | 9 | {
".py": 9
} | 13 | {
"Python": 43535
} | false | 2022-02-17T04:35:15 | {
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} | {
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BJe4oxHYPB | 2,020 | rejected | Winning the Lottery with Continuous Sparsification | [
"Pedro Savarese",
"Hugo Silva",
"Michael Maire"
] | [
"savarese@ttic.edu",
"hugoandradesilva664@gmail.com",
"mmaire@uchicago.edu"
] | OpenReview API | The Lottery Ticket Hypothesis from Frankle & Carbin (2019) conjectures that, for typically-sized neural networks, it is possible to find small sub-networks which train faster and yield superior performance than their original counterparts. The proposed algorithm to search for such sub-networks (winning tickets), Iterat... | Reject | null | 3 | [
{
"id": "Bklcxb3i5r",
"reviewer_signature": [
"ICLR.cc/2020/Conference/Paper2504/AnonReviewer4"
],
"rating": "3: Weak Reject",
"confidence": "",
"recommendation": "",
"soundness": "",
"presentation": "",
"contribution": "",
"correctness": "",
"summary": "",
"rev... | https://openreview.net/forum?id=BJe4oxHYPB | 1912.04427 | papers/BJe4oxHYPB.pdf | e17cb17cf63855e0d10013ad6db1e1153c2b6dd4cf5da813959a8879c120394f | 463,296 | openreview | https://github.com/lolemacs/continuous-sparsification | lolemacs/continuous-sparsification | 5bd4039f80724bbd71a78ba45d7efd35ea2e8e20 | repos/BJe4oxHYPB.zip | f324c6e01e535b95a3021fa560129d4d296c44f4e0cc009dd42fce19b84f37fe | 9,023 | 6 | {
".py": 6
} | 17 | {
"Python": 14108
} | false | 2022-06-10T10:39:52 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/winning-the-lottery-with-continuous-1"
} | {
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ryxDjjCqtQ | 2,019 | rejected | Deconfounding Reinforcement Learning in Observational Settings | [
"Chaochao Lu",
"José Miguel Hernández Lobato"
] | [
"cl641@cam.ac.uk",
"jmh233@cam.ac.uk"
] | OpenReview API | In this paper, we propose a general formulation to cope with a family of reinforcement learning tasks in observational settings, that is, learning good policies solely from the historical data produced by real environments with confounders (i.e., the factors affecting both actions and rewards). Based on the proposed ap... | null | Reject | 3 | [
{
"id": "Byg9XiEs3X",
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],
"rating": "4: Ok but not good enough - rejection",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"prese... | https://openreview.net/forum?id=ryxDjjCqtQ | 1812.10576 | papers/ryxDjjCqtQ.pdf | 69d8396b824ab8094caee9a975aadbd99b50a68991b0c7267f3b6c845f37a7fa | 1,784,580 | openreview | https://github.com/CausalRL/DRL | CausalRL/DRL | 0cd76bcbad8189ff3eb8ee750aaf6e992ce291a9 | repos/ryxDjjCqtQ.zip | ee1b041e506020300693f36eec1b45690f4a282a573cd0953c870e4ca18aad87 | 45,636 | 18 | {
".py": 18
} | 24 | {
"Python": 227459
} | false | 2019-04-13T22:45:57 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/deconfounding-reinforcement-learning-in"
} | {
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B1bgpzZAZ | 2,018 | rejected | ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions | [
"Soham Parikh",
"Ananya Sai",
"Preksha Nema",
"Mitesh M Khapra"
] | [
"sohamp@cse.iitm.ac.in",
"ananyasb@cse.iitm.ac.in",
"preksha@cse.iitm.ac.in",
"miteshk@cse.iitm.ac.in"
] | OpenReview API | The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given \{\textit{passage, question}\} pair and select one of the $n$ given options. The current state of the art model for this task first computes a query-aware representation for the passage and then \textit{selec... | Reject | null | 3 | [
{
"id": "HkHGUsPef",
"reviewer_signature": [
"ICLR.cc/2018/Conference/Paper990/AnonReviewer3"
],
"rating": "5: Marginally below acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
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"pre... | https://openreview.net/forum?id=B1bgpzZAZ | 1904.02651 | papers/B1bgpzZAZ.pdf | b48c4c1f871aa6f700303934a8668963d33fdcfca7e1e902e04859bbb5f48ab8 | 508,977 | openreview | https://github.com/sohamparikh/ElimiNet | sohamparikh/ElimiNet | f2a9f293fa2ce5470522444c8cfa6523c848392a | repos/B1bgpzZAZ.zip | 082dd1559dda2c785c6dafa812d121e07d489569010cc504be6e8c30ac400a0a | 15,164 | 8 | {
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JeiaHDawhb | 2,025 | rejected | Maximum Total Correlation Reinforcement Learning | [
"Bang You",
"Puze Liu",
"Huaping Liu",
"Jan Peters",
"Oleg Arenz"
] | [
"~Bang_You1",
"~Puze_Liu1",
"~Huaping_Liu3",
"~Jan_Peters3",
"~Oleg_Arenz1"
] | OpenReview API | Simplicity is a powerful inductive bias. In reinforcement learning, regularization is used for simpler policies, data augmentation for simpler representations, and sparse reward functions for simpler objectives, all that, with the underlying motivation to increase generalizability and robustness by focusing on the esse... | Reject | 4 | [
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"rating": 3,
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"presentation": 3,
"contribution": 1,
"confidence": 4,
"summary": "The paper proposes an auxiliary RL objective, MTC, that maximizes the tota... | https://openreview.net/forum?id=JeiaHDawhb | 2505.16734 | papers/JeiaHDawhb.pdf | 06c846adbac422813c8e2e3b292b0d92251a7c2cdbc0258157587faa17eeaaec | 454,123 | openreview | https://github.com/BangYou01/MTC | BangYou01/MTC | 7b4dfae88d6182e58fa7c7c85af5a6bfadc5f863 | repos/JeiaHDawhb.zip | ac2fc141ce9541ce029fba1050d6a435859f1aebb0857489c4d37c9ee8e1ff90 | 14,799 | 6 | {
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} | false | 2025-05-28T06:55:08 | {
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SHUQtRK0eU | 2,024 | rejected | Generalized Activation via Multivariate Projection | [
"Jiayun Li",
"Yuxiao Cheng",
"Zhuofan Xia",
"Yilin Mo",
"Gao Huang"
] | [
"~Jiayun_Li2",
"~Yuxiao_Cheng1",
"~Zhuofan_Xia2",
"~Yilin_Mo1",
"~Gao_Huang1"
] | OpenReview API | Activation functions are essential to introduce nonlinearity into neural networks, with the Rectified Linear Unit (ReLU) often favored for its simplicity and effectiveness. Motivated by the structural similarity between a shallow Feedforward Neural Network (FNN) and a single iteration of the Projected Gradient Descent ... | Reject | 3 | [
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"soundness": "3 good",
"presentation": "4 excellent",
"contribution": "3 good",
"confidence": "3: You are fairly con... | https://openreview.net/forum?id=SHUQtRK0eU | 2309.17194 | papers/SHUQtRK0eU.pdf | db6c5d626825d337d5d42ef32aa6a8844a3734174b54714b3d379e7960aba569 | 1,459,067 | openreview | https://github.com/ljy9912/mimo_nn | ljy9912/mimo_nn | f008dcc4ba9f1a9931d89e671654de244ede33b3 | repos/SHUQtRK0eU.zip | 5bb3a03a8c764fd117297f17e43c5257a0ccbe77928ff6e249f84921d7279bec | 11,571 | 8 | {
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} | false | 2024-05-20T04:00:07 | {
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2_BsVZ6R-ef | 2,023 | rejected | Analytical Composition of Differential Privacy via the Edgeworth Accountant | [
"Hua Wang",
"Sheng Gao",
"Huanyu Zhang",
"Milan Shen",
"Weijie J Su"
] | [
"~Hua_Wang7",
"~Sheng_Gao2",
"~Huanyu_Zhang2",
"~Milan_Shen1",
"~Weijie_J_Su1"
] | OpenReview API | Many modern machine learning algorithms are composed of simple private algorithms; thus, an increasingly important problem is to efficiently compute the overall privacy loss under composition. In this study, we introduce the Edgeworth Accountant, an analytical approach to composing differential privacy guarantees of pr... | Reject | null | 4 | [
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} | 15 | {
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} | false | 2022-06-09T01:57:56 | {
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JVsvIuMDE0Z | 2,022 | rejected | Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning | [
"Yi Zhao",
"Rinu Boney",
"Alexander Ilin",
"Juho Kannala",
"Joni Pajarinen"
] | [
"~Yi_Zhao6",
"~Rinu_Boney1",
"~Alexander_Ilin1",
"~Juho_Kannala1",
"~Joni_Pajarinen2"
] | OpenReview API | Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. However, depending on the quality of the offline dataset, such pre-trained agents may have limited performance and would further need to be fine-tuned online by interact... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=JVsvIuMDE0Z | 2210.13846 | papers/JVsvIuMDE0Z.pdf | 70f0482bd5c20f441a0698260c63a09406237927d910e6f7782cfc896f2f46e2 | 437,231 | openreview | https://github.com/zhaoyi11/adaptive_bc | zhaoyi11/adaptive_bc | d47226d4ffc9959ac0ed1337ddd7ebb787fc05e1 | repos/JVsvIuMDE0Z.zip | 5e9adbc418534e878989df1a2cbdb26b8a0ea69264aac3dd40ff236757d8de5f | 13,306 | 3 | {
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} | 14 | {
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} | false | 2022-07-04T08:28:37 | {
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crAi7c41xTh | 2,021 | rejected | Shape Matters: Understanding the Implicit Bias of the Noise Covariance | [
"Jeff Z. HaoChen",
"Colin Wei",
"Jason D. Lee",
"Tengyu Ma"
] | [
"~Jeff_Z._HaoChen1",
"~Colin_Wei1",
"~Jason_D._Lee1",
"~Tengyu_Ma1"
] | OpenReview API | The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect for training overparameterized models. Prior theoretical work largely focuses on spherical Gaussian noise, whereas empirical studies demonstrate the phenomenon that parameter-dependent noise --- induced by mini-batches or l... | Reject | null | 4 | [
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"... | https://openreview.net/forum?id=crAi7c41xTh | 2006.08680 | papers/crAi7c41xTh.pdf | fd62ac31d32f0bc4744b3ce6ba516caa8054d872b808125631bbee0a69774fe3 | 448,050 | openreview | https://github.com/jhaochenz96/noise-implicit-bias | jhaochenz96/noise-implicit-bias | 2b94db1672edf916851cd806c64c246000e2944e | repos/crAi7c41xTh.zip | ccf4a40dd299a9634fb0e5c21e326de86f0c002d39dbf6b01d5de3b498b436f7 | 11,874 | 8 | {
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} | 15 | {
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} | false | 2020-06-17T04:37:15 | {
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HJlzxgBtwH | 2,020 | rejected | Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack | [
"Francesco Croce",
"Matthias Hein"
] | [
"francesco91.croce@gmail.com",
"matthias.hein@uni-tuebingen.de"
] | OpenReview API | The evaluation of robustness against adversarial manipulations of neural networks-based classifiers is mainly tested with empirical attacks as the methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the $l_p$-norms for $... | Reject | null | 3 | [
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"rating": "6: Weak Accept",
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"rev... | https://openreview.net/forum?id=HJlzxgBtwH | 1907.02044 | papers/HJlzxgBtwH.pdf | 8bc18107b29805c3f4690737aec72d218d5c1a6751e7f0ddc4b2958ad1952170 | 3,264,275 | openreview | https://github.com/fra31/fab-attack | fra31/fab-attack | 7c3ef4cb4cd91ade912f06c683fbed8a4660464d | repos/HJlzxgBtwH.zip | 97d6d30d34229ac69bcb9dd17cc84210147b93fa773ed331ed9de3f0201573f2 | 18,226 | 7 | {
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} | 18 | {
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} | false | 2020-07-10T12:35:52 | {
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HJx7l309Fm | 2,019 | rejected | Actor-Attention-Critic for Multi-Agent Reinforcement Learning | [
"Shariq Iqbal",
"Fei Sha"
] | [
"shariqiqbal2810@gmail.com",
"feisha.work@gmail.com"
] | OpenReview API | Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism... | null | Reject | 3 | [
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"p... | https://openreview.net/forum?id=HJx7l309Fm | 1810.02912 | papers/HJx7l309Fm.pdf | dcdf7ac024d4ee05494c3f1a323a17bf4bde661b97c82292d9e5546ff9c50ccd | 739,740 | openreview | https://github.com/shariqiqbal2810/MAAC | shariqiqbal2810/MAAC | 6174a01251251e6778c4ada26bc8d9cd930e3856 | repos/HJx7l309Fm.zip | b3ddcd48636a5fda010f64338e06e632a7c600ac3f400a04207bbec175f99636 | 23,481 | 12 | {
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} | 30 | {
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} | false | 2022-05-29T16:14:16 | {
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SybqeKgA- | 2,018 | rejected | On Batch Adaptive Training for Deep Learning: Lower Loss and Larger Step Size | [
"Runyao Chen",
"Kun Wu",
"Ping Luo"
] | [
"chenrunyao14@mails.ucas.ac.cn",
"WuKun14@mails.ucas.ac.cn",
"luop@ict.ac.cn"
] | OpenReview API | Mini-batch gradient descent and its variants are commonly used in deep learning. The principle of mini-batch gradient descent is to use noisy gradient calculated on a batch to estimate the real gradient, thus balancing the computation cost per iteration and the uncertainty of noisy gradient. However, its batch size is ... | Reject | null | 3 | [
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"rating": "5: Marginally below acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
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"soun... | https://openreview.net/forum?id=SybqeKgA- | null | papers/SybqeKgA-.pdf | e98e153f12732b25dfda3eff31c04e5ea49ba7daf66402734caf1799a3bc7ab7 | 1,551,207 | openreview | https://github.com/thomasyao3096/Batch_Adaptive_Framework | thomasyao3096/Batch_Adaptive_Framework | 2244eebc33938ef816120a80eb2560192ec08b41 | repos/SybqeKgA-.zip | f53cd8052f21d0cf8cd9de2c3e605ddbd47b1f47c59cda5d3abaa5ed39d86dc9 | 11,261 | 4 | {
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} | 19 | {
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} | false | 2017-11-12T02:27:36 | {
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ODibPQmeP1 | 2,026 | rejected | CHARM: Calibrating Reward Models With Chatbot Arena Scores | [
"Xiao Zhu",
"Chenmien Tan",
"Pinzhen Chen",
"Rico Sennrich",
"Yanlin Zhang",
"Hanxu Hu"
] | [
"~Xiao_Zhu4",
"~Chenmien_Tan1",
"~Pinzhen_Chen1",
"~Rico_Sennrich1",
"~Yanlin_Zhang1",
"~Hanxu_Hu1"
] | OpenReview API | Reward models (RMs) play a crucial role in Reinforcement Learning from Human Feedback by serving as proxies for human preferences in aligning large language models. However, they suffer from various biases which could lead to reward hacking. In this paper, we identify a model preference bias in RMs, where they systemat... | Reject | 4 | [
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"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper identifies \"Model Preference Bias\" in reward models, which is... | https://openreview.net/forum?id=ODibPQmeP1 | 2504.10045 | papers/ODibPQmeP1.pdf | a83b6bf53aa1f23dda4b9d2b24478039d97acaeb0fbb78db1b745a65f814d236 | 1,281,082 | openreview | https://github.com/HexagonStar/CHARM | HexagonStar/CHARM | 719f0aef03a1eb8381bad450b0d991455b91563a | repos/ODibPQmeP1.zip | 2629f6448f327bc04d0ff4af9b127e6069eb7b624197d5117372fc5e7dc3cb3f | 21,492 | 12 | {
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pogJXugbN8 | 2,024 | rejected | BAFFLE: A Baseline of Backpropagation-Free Federated Learning | [
"Haozhe Feng",
"Tianyu Pang",
"Chao Du",
"Wei Chen",
"Shuicheng YAN",
"Min Lin"
] | [
"~Haozhe_Feng1",
"~Tianyu_Pang1",
"~Chao_Du1",
"~Wei_Chen34",
"~Shuicheng_YAN3",
"~Min_Lin1"
] | OpenReview API | Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical applications, but its standard training paradigm requires the clients to backpropagate through the model to compute gradients. Since these ... | Reject | 4 | [
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"rating": "3: reject, not good enough",
"soundness": "2 fair",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in your assessment, but... | https://openreview.net/forum?id=pogJXugbN8 | 2301.12195 | papers/pogJXugbN8.pdf | a0cf61126a3f28bb76de4ec658d5bdf238284693e3dcfa510292575be86fb391 | 907,149 | openreview | https://github.com/FengHZ/BAFFLE | FengHZ/BAFFLE | b59374505eb21c6d3ae2c6a513556c05d2d1dce9 | repos/pogJXugbN8.zip | 06c2d593027c587a16f587b7a7e4aff8f309e4f23fb1e891870084d8ac0a82c9 | 15,249 | 8 | {
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} | 15 | {
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} | false | 2023-02-09T11:41:10 | {
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w1w4dGJ4qV | 2,023 | rejected | The Benefits of Model-Based Generalization in Reinforcement Learning | [
"Kenny John Young",
"Aditya Ramesh",
"Louis Kirsch",
"Jürgen Schmidhuber"
] | [
"~Kenny_John_Young1",
"~Aditya_Ramesh2",
"~Louis_Kirsch1",
"~Jürgen_Schmidhuber1"
] | OpenReview API | Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind of model, which has proved extremely effective at improving the stability and effi... | Reject | null | 4 | [
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} | false | 2023-01-13T19:03:22 | {
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-3Qj7Jl6UP5 | 2,022 | rejected | The magnitude vector of images | [
"Michael F Adamer",
"Leslie O'Bray",
"Edward De Brouwer",
"Bastian Rieck",
"Karsten Borgwardt"
] | [
"~Michael_F_Adamer1",
"~Leslie_O'Bray1",
"~Edward_De_Brouwer1",
"~Bastian_Rieck1",
"~Karsten_Borgwardt2"
] | OpenReview API | The magnitude of a finite metric space is a recently-introduced invariant quantity. Despite beneficial theoretical and practical properties, such as a general utility for outlier detection, and a close connection to Laplace radial basis kernels, magnitude has received little attention by the machine learning community ... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=-3Qj7Jl6UP5 | 2110.15188 | papers/-3Qj7Jl6UP5.pdf | 9d22ebc02f37c7f8999316f15db468927be2c72583fa8bfde309563ab9a6d3bc | 1,553,416 | openreview | https://github.com/MikeAdamer/mag-metric | MikeAdamer/mag-metric | 1f33528faf55d32d4cd8cfa983bbc95cf9b3dce0 | repos/-3Qj7Jl6UP5.zip | ba7b0cc320fa111f048e036608739e858eb4e488a01d2fb2f04471c6e7e19ae1 | 21,211 | 10 | {
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} | 16 | {
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} | false | 2022-09-30T14:26:52 | {
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BJxQxeBYwH | 2,020 | rejected | Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification | [
"Ting Chen",
"Song Bian",
"Yizhou Sun"
] | [
"iamtingchen@gmail.com",
"biansonghz@gmail.com",
"yzsun@cs.ucla.edu"
] | OpenReview API | Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning and how sophisticated the learned graph functions are. In this work, we propose a dissection of GNNs... | Reject | null | 3 | [
{
"id": "Hye94W7M5r",
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],
"rating": "6: Weak Accept",
"confidence": "",
"recommendation": "",
"soundness": "",
"presentation": "",
"contribution": "",
"correctness": "",
"summary": "",
"rev... | https://openreview.net/forum?id=BJxQxeBYwH | 1905.04579 | papers/BJxQxeBYwH.pdf | 4c0c8fba22739b685bbbbe1d0504875aa20b835d46e1ff80b4aaf3696f27feba | 1,431,009 | openreview | https://github.com/chentingpc/gfn | chentingpc/gfn | b598eb77f62680e62e2adb558b158505b2a3926a | repos/BJxQxeBYwH.zip | e84c5442f37fd839767ca4446d3ae7bada5b171133c4c64c54094fec228983d6 | 16,195 | 9 | {
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} | 21 | {
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} | false | 2020-05-05T01:49:13 | {
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S1giVsRcYm | 2,019 | rejected | Count-Based Exploration with the Successor Representation | [
"Marlos C. Machado",
"Marc G. Bellemare",
"Michael Bowling"
] | [
"machado@ualberta.ca",
"bellemare@google.com",
"mbowling@ualberta.ca"
] | OpenReview API | The problem of exploration in reinforcement learning is well-understood in the tabular case and many sample-efficient algorithms are known. Nevertheless, it is often unclear how the algorithms in the tabular setting can be extended to tasks with large state-spaces where generalization is required. Recent promising deve... | null | Reject | 3 | [
{
"id": "Hyl3ZDxqh7",
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"ICLR.cc/2019/Conference/Paper29/AnonReviewer2"
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"rating": "5: Marginally below acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soun... | https://openreview.net/forum?id=S1giVsRcYm | 1807.11622 | papers/S1giVsRcYm.pdf | 3eaf0a7a954c68f3833685770709e661b8d0aa8da03f9151a4f72dd77edb4cd0 | 3,941,702 | openreview | https://github.com/mcmachado/count_based_exploration_sr | mcmachado/count_based_exploration_sr | e4d657eb498ca84a703ddd7ec426d908adc67a72 | repos/S1giVsRcYm.zip | 14563765715ec9109131eda98b3fa3c9537c66cf80d885bfb3f7e553abe5d450 | 38,266 | 23 | {
".py": 23
} | 31 | {
"Python": 93470
} | false | 2019-07-01T20:22:50 | {
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} | {
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} |
B1tExikAW | 2,018 | rejected | LatentPoison -- Adversarial Attacks On The Latent Space | [
"Antonia Creswell",
"Biswa Sengupta",
"Anil A. Bharath"
] | [
"ac2211@ic.ac.uk",
"b.sengupta@imperial.ac.uk",
"a.bharath@imperial.ac.uk"
] | OpenReview API | Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits. With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, un... | Reject | null | 3 | [
{
"id": "B1xzWeqgG",
"reviewer_signature": [
"ICLR.cc/2018/Conference/Paper153/AnonReviewer3"
],
"rating": "4: Ok but not good enough - rejection",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundne... | https://openreview.net/forum?id=B1tExikAW | 1711.02879 | papers/B1tExikAW.pdf | 9eb7ea64274e6f133f2e993a4bf7b7e6dc0c50356c73ecff60f9756082ad202a | 8,626,408 | openreview | https://github.com/ToniCreswell/Adversarial-Attack-On-Latent-Space | ToniCreswell/Adversarial-Attack-On-Latent-Space | 812de9a3c6a6be8ad6e2923625d09b900fe07a00 | repos/B1tExikAW.zip | 49ed91c4148515ca56b70e9c8b8ef4f686c21bcff7f6f9243919e2b4f61b9c55 | 18,206 | 6 | {
".py": 5,
".ipynb": 1
} | 22 | {
"Python": 37809,
"Jupyter Notebook": 2909
} | false | 2022-12-07T23:58:00 | {
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Bibt0JTvpx | 2,026 | rejected | EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation | [
"Jinghan Jia",
"Hadi Reisizadeh",
"Chongyu Fan",
"Nathalie Baracaldo",
"Mingyi Hong",
"Sijia Liu"
] | [
"~Jinghan_Jia1",
"~Hadi_Reisizadeh1",
"~Chongyu_Fan1",
"~Nathalie_Baracaldo1",
"~Mingyi_Hong1",
"~Sijia_Liu1"
] | OpenReview API | Large language models (LLMs) have shown remarkable reasoning capabilities when trained with chain-of-thought (CoT) supervision. However, the long and verbose CoT traces, especially those distilled from large reasoning models (LRMs) such as DeepSeek-R1, significantly increase training costs during the distillation proce... | Reject | 3 | [
{
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],
"rating": 2,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "The paper proposes EPiC, an edge-preserving condensation method that prune... | https://openreview.net/forum?id=Bibt0JTvpx | 2506.04205 | papers/Bibt0JTvpx.pdf | 65d0f0acf1f8948c088e9aca92e717647e1cf86d6478700e52562d568a1aa382 | 2,033,741 | openreview | https://github.com/OPTML-Group/EPiC | OPTML-Group/EPiC | 54610896cb4659a7a05920cf8437ea070cf84d2f | repos/Bibt0JTvpx.zip | 05d59008616105ced26ad93f3c2474f2004d00fa12c526e77dceb26cbb1b3c0a | 50,941 | 20 | {
".py": 20
} | 43 | {
"Python": 96709
} | false | 2025-06-11T18:08:47 | {
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yLmcYLP3Yd | 2,025 | rejected | Discrete Neural Algorithmic Reasoning | [
"Gleb Rodionov",
"Liudmila Prokhorenkova"
] | [
"~Gleb_Rodionov1",
"~Liudmila_Prokhorenkova1"
] | OpenReview API | Neural algorithmic reasoning aims to capture computations with neural networks via learning the models to imitate the execution of classic algorithms. While common architectures are expressive enough to contain the correct model in the weights space, current neural reasoners are struggling to generalize well on out-of-... | Reject | 5 | [
{
"id": "Z9AJMN06DZ",
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],
"rating": 8,
"soundness": 4,
"presentation": 4,
"contribution": 3,
"confidence": 4,
"summary": "This paper introduces a novel approach to neural algorithmic reasoning by ... | https://openreview.net/forum?id=yLmcYLP3Yd | 2402.11628 | papers/yLmcYLP3Yd.pdf | ae3b1317f66c43efa8a65f094d15a754865be2e3bc4187f3db6892ebe110e005 | 426,356 | openreview | https://github.com/yandex-research/dnar | yandex-research/dnar | 12f3f0bd0a70568386e43b2d841422f28ed53698 | repos/yLmcYLP3Yd.zip | 76cf854f1efe2f7d4ae01fb75ae2602795cd5ea6d55c58919cbecf270d66a157 | 17,641 | 7 | {
".py": 7
} | 15 | {
"Python": 38447
} | false | 2024-09-10T15:48:36 | {
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} | {
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} | |
VLnODGVVAsL | 2,023 | rejected | Anchor Sampling for Federated Learning with Partial Client Participation | [
"Feijie Wu",
"Song Guo",
"Zhihao Qu",
"Shiqi He",
"Ziming Liu"
] | [
"~Feijie_Wu1",
"~Song_Guo5",
"~Zhihao_Qu1",
"~Shiqi_He1",
"~Ziming_Liu1"
] | OpenReview API | In federated learning, the support of partial client participation offers a flexible training strategy, but it deteriorates the model training efficiency. In this paper, we propose a framework FedAMD to improve the convergence property and maintain flexibility. The core idea is anchor sampling, which disjoints the part... | Reject | null | 3 | [
{
"id": "1Ah36DRJdF",
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],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piece... | https://openreview.net/forum?id=VLnODGVVAsL | 2206.05891 | papers/VLnODGVVAsL.pdf | 2f51defed0fe2dadd68ec7ed4dd642ce49e267a41b3effcb1ff6d0deab199e7b | 1,395,729 | openreview | https://github.com/HarliWu/FedAMD | HarliWu/FedAMD | 73596d55e15a3ef6f404e5a313f50bc05a328c55 | repos/VLnODGVVAsL.zip | c0a44531ab2903feef431e58f2eb4b87e4397d8f1933e0a8312dba08c8047aea | 28,071 | 13 | {
".py": 13
} | 20 | {
"Python": 87743
} | false | 2024-04-09T17:55:44 | {
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} | {
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7Z7u2z1Ornl | 2,022 | rejected | Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets | [
"David W Zhang",
"Gertjan J. Burghouts",
"Cees G. M. Snoek"
] | [
"~David_W_Zhang1",
"~Gertjan_J._Burghouts1",
"~Cees_G._M._Snoek1"
] | OpenReview API | This paper aims for set-to-hypergraph prediction, where the goal is to infer the set of relations for a given set of entities. This is a common abstraction for applications in particle physics, biological systems and combinatorial optimization. We address two common scaling problems encountered in set-to-hypergraph tas... | Reject | null | 4 | [
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"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=7Z7u2z1Ornl | 2106.13919 | papers/7Z7u2z1Ornl.pdf | 7cb5550039f5258201fc6903989fd4ca391dba4a9ac50056e3e05d832f07af94 | 497,702 | openreview | https://github.com/davzha/recurrently_predicting_hypergraphs | davzha/recurrently_predicting_hypergraphs | c2b9a4959a1b9e3bac05ad3e5264029ccad2447d | repos/7Z7u2z1Ornl.zip | f924f1cdd07be8dfb94f21a4f4cb4c71a681edf06fd4a3f32775c1ff327bc15e | 25,112 | 15 | {
".py": 15
} | 17 | {
"Python": 60006
} | false | 2021-09-21T09:02:49 | {
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} | {
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yeeS_HULL7Z | 2,021 | rejected | Attention-Based Clustering: Learning a Kernel from Context | [
"Samuel Coward",
"Erik Visse-Martindale",
"Chithrupa Ramesh"
] | [
"~Samuel_Coward1",
"erik.visse-martindale@uk.zuken.com",
"~Chithrupa_Ramesh1"
] | OpenReview API | In machine learning, no data point stands alone. We believe that context is an underappreciated concept in many machine learning methods. We propose Attention-Based Clustering (ABC), a neural architecture based on the attention mechanism, which is designed to learn latent representations that adapt to context within an... | Reject | null | 4 | [
{
"id": "SjLQ0S-7LK",
"reviewer_signature": [
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],
"rating": "5: Marginally below acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"pr... | https://openreview.net/forum?id=yeeS_HULL7Z | 2010.01040 | papers/yeeS_HULL7Z.pdf | fa9d14e53527c1987340d84f5e913c8bf4ca4f66bf236df811c54929caf461b7 | 396,620 | openreview | https://github.com/DramaCow/ABC | DramaCow/ABC | 20e084d2b4d9b3fb0456c9927cc96174c0fd1657 | repos/yeeS_HULL7Z.zip | 8bd8de7b6c6e52f4da379206ba570ff77d0ddd8316c02319b1424d7c7a184066 | 23,618 | 16 | {
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} | 20 | {
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"Shell": 194
} | false | 2024-09-13T11:36:50 | {
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S1lF8xHYwS | 2,020 | rejected | Unsupervised Domain Adaptation through Self-Supervision | [
"Yu Sun",
"Eric Tzeng",
"Trevor Darrell",
"Alexei A. Efros"
] | [
"yusun@berkeley.edu",
"etzeng@eecs.berkeley.edu",
"trevor@eecs.berkeley.edu",
"efros@eecs.berkeley.edu"
] | OpenReview API | This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data. Like much of previous work, we seek to align the learned representations of the source and target domains wh... | Reject | null | 3 | [
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"rating": "6: Weak Accept",
"confidence": "",
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"rev... | https://openreview.net/forum?id=S1lF8xHYwS | 1909.11825 | papers/S1lF8xHYwS.pdf | 1c40f9ea0702869dd2834daf98891e8a8ae20111d5eab9e25410d219ff355f19 | 6,346,299 | openreview | https://github.com/yueatsprograms/uda_release | yueatsprograms/uda_release | b256316283e74b5d1f16777f029c384ee9b6e2e7 | repos/S1lF8xHYwS.zip | 116a62eb06d838bea684a05e1410d7bf7ad8ab1b8ca1b06b54ef3cadc4c12a33 | 24,041 | 29 | {
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} | 21 | {
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} | false | 2021-10-06T17:41:03 | {
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H1lnJ2Rqt7 | 2,019 | rejected | LARGE BATCH SIZE TRAINING OF NEURAL NETWORKS WITH ADVERSARIAL TRAINING AND SECOND-ORDER INFORMATION | [
"Zhewei Yao",
"Amir Gholami",
"Kurt Keutzer",
"Michael Mahoney"
] | [
"zheweiy@berkeley.edu",
"amirgh@berkeley.edu",
"keutzer@berkeley.edu",
"mmahoney@stat.berkeley.edu"
] | OpenReview API | Stochastic Gradient Descent (SGD) methods using randomly selected batches are widely-used to train neural network (NN) models. Performing design exploration to find the best NN for a particular task often requires extensive training with different models on a large dataset, which is very computationally expensive. The... | null | Reject | 3 | [
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"rating": "4: Ok but not good enough - rejection",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
"re... | https://openreview.net/forum?id=H1lnJ2Rqt7 | 1810.01021 | papers/H1lnJ2Rqt7.pdf | eeb0d26bc21ceac77f642114a6564744a91686ec797a5a1131febc95691a8143 | 721,965 | openreview | https://github.com/amirgholami/HessianFlow | amirgholami/HessianFlow | 644fc8a57472f3895fc21ba68357e46ad723beec | repos/H1lnJ2Rqt7.zip | 74e706620eb7f530638b28ad68b540f83bfa1bd8d8d434b824782a1901960504 | 32,302 | 14 | {
".py": 14
} | 35 | {
"Python": 44843
} | false | 2020-01-15T06:36:26 | {
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rJ7yZ2P6- | 2,018 | rejected | Enhance Word Representation for Out-of-Vocabulary on Ubuntu Dialogue Corpus | [
"JIANXIONG DONG",
"Jim Huang"
] | [
"jdongca2003@gmail.com",
"ccjimhuang@gmail.com"
] | OpenReview API | Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end
deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is
the large number of out-of-vocabulary words. In this paper we proposed an algorithm which combines th... | Reject | null | 3 | [
{
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"reviewer_signature": [
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"rating": "3: Clear rejection",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
"recommendation": "",
... | https://openreview.net/forum?id=rJ7yZ2P6- | 1802.02614 | papers/rJ7yZ2P6-.pdf | c5060bc86dca3475ba979043a06f7dd348e6c5ce568df078379ebbd8a515259f | 327,399 | openreview | https://github.com/jdongca2003/next_utterance_selection | jdongca2003/next_utterance_selection | 7491e972d58412c175166f6564d77b7436e71a87 | repos/rJ7yZ2P6-.zip | 773b6feb8049ca8dc1e0ce3d2fb4bc77dd3a4dd2142e680369b67f9d14437d9f | 23,916 | 10 | {
".py": 7,
".sh": 3
} | 24 | {
"Python": 46161,
"Shell": 2365
} | false | 2018-05-07T05:07:38 | {
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bDee2EgvWJ | 2,026 | rejected | Approximate Message Passing for Bayesian Neural Networks | [
"Romeo Sommerfeld",
"Christian Helms",
"Jan Niklas Groeneveld",
"Rainer Schlosser",
"Ralf Herbrich"
] | [
"~Romeo_Sommerfeld1",
"~Christian_Helms1",
"~Jan_Niklas_Groeneveld1",
"~Rainer_Schlosser1",
"~Ralf_Herbrich1"
] | OpenReview API | Bayesian methods for learning predictive models have the ability to consider both sources of uncertainty (i.e., data and model uncertainty) within a single framework and thereby provide a powerful tool for decision-making. Bayesian neural networks (BNNs) hold great potential for training data efficiency due to full un... | Reject | 4 | [
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"rating": 4,
"soundness": 2,
"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "This paper trains BNNs using “approximate message passing on factor graph... | https://openreview.net/forum?id=bDee2EgvWJ | 2501.15573 | papers/bDee2EgvWJ.pdf | d8c03d50a4202cf7a5f7fddd5623d4d2fc085b6c0cecea79b7135e578188bd61 | 2,803,386 | openreview | https://github.com/christian-helms/mpbnns | christian-helms/mpbnns | 85f12084c7ab9d89acb9628b02413e841c7346f7 | repos/bDee2EgvWJ.zip | ebbf2273e0467ed6483c8c79ce6b024267b2bfa88e09167dff7e7eeac3efe2fc | 57,358 | 21 | {
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} | 54 | {
"Julia": 180263,
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} | false | 2025-01-26T17:12:50 | {
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} | {
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} | |
lAXlDAdan5 | 2,025 | rejected | Accelerating Error Correction Code Transformers | [
"Matan Levy",
"Yoni Choukroun",
"Lior Wolf"
] | [
"~Matan_Levy2",
"~Yoni_Choukroun1",
"~Lior_Wolf1"
] | OpenReview API | Error correction codes (ECC) are crucial for ensuring reliable information transmission in communication systems. Choukroun & Wolf (2022b) recently introduced the Error Correction Code Transformer (ECCT), which has demonstrated promising performance across various transmission channels and families of codes. However, i... | Reject | 6 | [
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"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "This paper introduces simplifications to the Error Correction Code Transfo... | https://openreview.net/forum?id=lAXlDAdan5 | 2410.05911 | papers/lAXlDAdan5.pdf | 521ebc1740dc0f78a95b3742ca1a5e88013508acbf748a9022196483c13eacc7 | 2,465,131 | openreview | https://github.com/mlaetvayn/AECCT | mlaetvayn/AECCT | d66d5dc9312bde0484c126eba0a1dd857fcd2418 | repos/lAXlDAdan5.zip | 4457907076cdc95c4a02315d7a8ec7a510ec7ee532a3c5c40bec084d086239eb | 17,429 | 5 | {
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} | 16 | {
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} | false | 2024-10-16T09:16:41 | {
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"pwc_url": "https://paperswithcode.com/paper/accelerating-error-correction-code"
} | {
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} | |
YkEW5TabYN | 2,024 | rejected | Perturbed examples reveal invariances shared by language models | [
"Ruchit Rawal",
"Mariya Toneva"
] | [
"~Ruchit_Rawal1",
"~Mariya_Toneva1"
] | OpenReview API | An explosion of work in language is leading to ever-increasing numbers of available natural language processing models, with little understanding of how new models compare to better-understood models. One major reason for this difficulty is saturating benchmark datasets, which may not reflect well differences in model ... | Reject | 4 | [
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"id": "o1z5EB6ftX",
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],
"rating": "8: accept, good paper",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "3: You are fairly confident in your assessment. I... | https://openreview.net/forum?id=YkEW5TabYN | 2311.04166 | papers/YkEW5TabYN.pdf | f61900781619f6513465cbf29a7de809e536e6853c69af1c4a73fcf82e6d072e | 1,219,604 | openreview | https://github.com/bridge-ai-neuro/shared_invariances_acl | bridge-ai-neuro/shared_invariances_acl | f2615b69f213931488074d842514cef4ee139327 | repos/YkEW5TabYN.zip | 77c3c5ec76609da9bca135acb87c8580e7f0ef043a45bc89d0635c2857a56e55 | 20,792 | 14 | {
".py": 11,
".sh": 3
} | 16 | {
"Python": 48260,
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} | false | 2024-06-09T10:05:31 | {
"method": "exact_normalized_title",
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"pwc_url": "https://paperswithcode.com/paper/perturbed-examples-reveal-invariances-shared"
} | {
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} | |
CEhy-i7_KfC | 2,023 | rejected | Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning | [
"Manuel Goulão",
"Arlindo L. Oliveira"
] | [
"~Manuel_Goulão1",
"~Arlindo_L._Oliveira1"
] | OpenReview API | The Vision Transformer architecture has shown to be competitive in the computer vision (CV) space where it has dethroned convolution-based networks in several benchmarks. Nevertheless, Convolutional Neural Networks (CNN) remain the preferential architecture for the representation module in Reinforcement Learning. In th... | Reject | null | 5 | [
{
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"ICLR.cc/2023/Conference/Paper3071/Reviewer_rKFV"
],
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=CEhy-i7_KfC | 2209.10901 | papers/CEhy-i7_KfC.pdf | 70eb5a4040f93871f4aca16d008325a6d8980b5e0c8683a4e040275976a0cb68 | 393,689 | openreview | https://github.com/mgoulao/TOV-VICReg | mgoulao/TOV-VICReg | 535b1643134b407ca40ca2c6553e04b50d3968ae | repos/CEhy-i7_KfC.zip | ca3a98c07a3ef237691d3f0301ed573eb8785863038ba5bf1659cc8c10ab99b9 | 24,578 | 13 | {
".py": 11,
".sh": 2
} | 20 | {
"Python": 59289,
"Shell": 2501
} | false | 2022-09-22T10:33:19 | {
"method": "exact_normalized_title",
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"pwc_url": "https://paperswithcode.com/paper/pretraining-the-vision-transformer-using-self"
} | {
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} |
FKotzp6PZJw | 2,021 | rejected | On the Estimation Bias in Double Q-Learning | [
"Zhizhou Ren",
"Guangxiang Zhu",
"Beining Han",
"Jianglun Chen",
"Chongjie Zhang"
] | [
"~Zhizhou_Ren1",
"~Guangxiang_Zhu1",
"~Beining_Han1",
"~Jianglun_Chen2",
"~Chongjie_Zhang1"
] | OpenReview API | Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operator. Its variants in the deep Q-learning paradigm have shown great promise in producing reliable value prediction and improving learning performance. However, as shown by prio... | Reject | null | 4 | [
{
"id": "07u6jF0r1bB",
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"ICLR.cc/2021/Conference/Paper1410/AnonReviewer1"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"... | https://openreview.net/forum?id=FKotzp6PZJw | 2109.14419 | papers/FKotzp6PZJw.pdf | 48483f43dd599eae7d07bd52ed6c742e38f3bfde6167ff91e1549abd6e1e6547 | 1,085,232 | openreview | https://github.com/Stilwell-Git/Doubly-Bounded-Q-Learning | Stilwell-Git/Doubly-Bounded-Q-Learning | 028ee629781f3fc7daa149b5b838884cfa09fcd1 | repos/FKotzp6PZJw.zip | b7abd2079ad9c96c5b328240dc13250ed8b887a6d2cdf02d67d12f834847bf37 | 22,158 | 18 | {
".py": 17,
".cpp": 1
} | 20 | {
"Python": 40506,
"C++": 4793
} | false | 2022-11-14T14:00:54 | {
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} | {
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} |
H1lKd6NYPS | 2,020 | rejected | Online Meta-Critic Learning for Off-Policy Actor-Critic Methods | [
"Wei Zhou",
"Yiying Li",
"Yongxin Yang",
"Huaimin Wang",
"Timothy M. Hospedales"
] | [
"zhouwei14@nudt.edu.cn",
"liyiying10@nudt.edu.cn",
"yongxin.yang@ed.ac.uk",
"hmwang@nudt.edu.cn",
"t.hospedales@ed.ac.uk"
] | OpenReview API | Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks. Normally, the critic’s action-value function is updated using temporal-difference, and the critic in turn provides a loss for the actor that trains it to take actions with higher expected return. In this paper, we... | Reject | null | 3 | [
{
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],
"rating": "3: Weak Reject",
"confidence": "",
"recommendation": "",
"soundness": "",
"presentation": "",
"contribution": "",
"correctness": "",
"summary": "",
"revi... | https://openreview.net/forum?id=H1lKd6NYPS | 2003.05334 | papers/H1lKd6NYPS.pdf | 3f73a188b1d752d13af721ddf2d41d99a57316b24523da888689f411b6a5ad89 | 7,208,362 | openreview | https://github.com/zwfightzw/Meta-Critic | zwfightzw/Meta-Critic | d8045d66fa82d5035868b82e6bd9cbdbe6fa5955 | repos/H1lKd6NYPS.zip | 4ec08efa2248202c6989da1920d4263836bab039a868c110ccb47ac8068e5ed6 | 26,608 | 12 | {
".py": 12
} | 22 | {
"Python": 78982
} | false | 2020-10-19T06:25:17 | {
"method": "exact_normalized_title",
"pwc_official": true,
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"pwc_url": "https://paperswithcode.com/paper/online-meta-critic-learning-for-off-policy-1"
} | {
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} |
Sygx4305KQ | 2,019 | rejected | Small steps and giant leaps: Minimal Newton solvers for Deep Learning | [
"Joao Henriques",
"Sebastien Ehrhardt",
"Samuel Albanie",
"Andrea Vedaldi"
] | [
"joao@robots.ox.ac.uk",
"hyenal@robots.ox.ac.uk",
"albanie@robots.ox.ac.uk",
"vedali@robots.ox.ac.uk"
] | OpenReview API | We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard for... | null | Reject | 3 | [
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"rating": "7: Good paper, accept",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
"recommendation": "... | https://openreview.net/forum?id=Sygx4305KQ | 1805.08095 | papers/Sygx4305KQ.pdf | d41e9bfffb0357512e05446983925195dd5f02a8abd22512172c6a81369dcbb5 | 2,346,524 | openreview | https://github.com/jotaf98/curveball | jotaf98/curveball | 1dc37325382c12e3fc9b2e7e27c47e6d7a17021a | repos/Sygx4305KQ.zip | 7d7433458348ca7b73a461edfc7b926a18453def06cd9543c8227492bb79c16a | 43,815 | 35 | {
".m": 21,
".cu": 10,
".cpp": 2,
".sh": 1,
".hpp": 1
} | 37 | {
"MATLAB": 58871,
"Cuda": 42788,
"C++": 1212,
"Shell": 548
} | false | 2018-10-29T13:59:54 | {
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} | {
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} |
HyEi7bWR- | 2,018 | rejected | Orthogonal Recurrent Neural Networks with Scaled Cayley Transform | [
"Kyle Helfrich",
"Devin Willmott",
"Qiang Ye"
] | [
"kyle.helfrich@uky.edu",
"devin.willmott@uky.edu",
"qiang.ye@uky.edu"
] | OpenReview API | Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose a si... | Reject | null | 3 | [
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],
"rating": "7: Good paper, accept",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"presentation": "",
... | https://openreview.net/forum?id=HyEi7bWR- | 1707.09520 | papers/HyEi7bWR-.pdf | 31ec6aad5b3148eb8a25485574a1b299fcf6cfc7bd774161abf6fd7fcfdbc71d | 676,036 | openreview | https://github.com/SpartinStuff/scoRNN | SpartinStuff/scoRNN | d390c9bac62c510963ff90d386fb02beccff0a1e | repos/HyEi7bWR-.zip | c89c8a0d7597fa0f6d04b4f31b3a0d3f1ba9f5b19be80c929c69ba2c42de1726 | 12,462 | 4 | {
".py": 4
} | 26 | {
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} | false | 2024-05-24T09:14:20 | {
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} | {
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} |
oVd1y7ilTk | 2,026 | rejected | On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning | [
"Magdalena Proszewska",
"Nikolay Malkin",
"Siddharth N"
] | [
"~Magdalena_Proszewska1",
"~Nikolay_Malkin1",
"~Siddharth_N1"
] | OpenReview API | Diffusion autoencoders (DAs) are variants of diffusion generative models that use an input-dependent latent variable to capture representations alongside the diffusion process. These representations can be used for tasks such as downstream classification, controllable generation, and interpolation. However, the generat... | Reject | 4 | [
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],
"rating": 4,
"soundness": 2,
"presentation": 2,
"contribution": 1,
"confidence": 5,
"summary": "The authors propose a diffusion autoencoder framework, DMZ, with carefully... | https://openreview.net/forum?id=oVd1y7ilTk | 2506.00136 | papers/oVd1y7ilTk.pdf | 79bed6630dc80a05b2da429c077a3f2632d1fffe35ab7f3b45ed02b0f854630f | 9,032,494 | openreview | https://github.com/exlab-research/dmz | exlab-research/dmz | 1d740e18e68348e3dd43acfda37508d9aed67a8a | repos/oVd1y7ilTk.zip | 8f75859b96c80c6a3819d8c6925123ec98fcbe1ae043b67712f445b2d4492e23 | 47,223 | 20 | {
".py": 20
} | 56 | {
"Python": 149055
} | false | 2025-09-25T09:44:51 | {
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} | {
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} | |
zCncHdGsOa | 2,025 | rejected | Efficient optimization with orthogonality constraint: a randomized Riemannian submanifold method | [
"Andi Han",
"Pierre-Louis Poirion",
"Akiko Takeda"
] | [
"~Andi_Han1",
"~Pierre-Louis_Poirion1",
"~Akiko_Takeda2"
] | OpenReview API | Optimization with orthogonality constraints frequently arise in various fields such as machine learning, signal processing and computer vision. Riemannian optimization offers a powerful framework for solving these problems by equipping the constraint set with a Riemannian manifold structure and performing optimization ... | Reject | 4 | [
{
"id": "7SUvItPomu",
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],
"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 5,
"summary": "The paper proposes a randomized Riemannian submanifold method for solving ... | https://openreview.net/forum?id=zCncHdGsOa | 2505.12378 | papers/zCncHdGsOa.pdf | b3a8931dc232e0663dd4d50438dd76c65caeea97600ddff6b216e203c4791663 | 1,255,804 | openreview | https://github.com/andyjm3/RSDM | andyjm3/RSDM | ac14994038a100b1047a324c7931b529ac0397f5 | repos/zCncHdGsOa.zip | 6503c40e738e953eea87e17120ce9996b1accc5ceba55efd8636e57300993994 | 15,170 | 6 | {
".py": 6
} | 17 | {
"Python": 30936
} | false | 2025-05-21T10:10:56 | {
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} | {
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} | |
tB7p0SM5TH | 2,024 | rejected | GraSP: Simple yet Effective Graph Similarity Predictions | [
"Haoran Zheng",
"Jieming Shi"
] | [
"~Haoran_Zheng1",
"~Jieming_Shi1"
] | OpenReview API | Graph similarity computation (GSC) is considered one of the essential operations because of its wide range of applications in various fields. Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) are the most popular graph similarity metrics. However, calculating exact GED and MCS is a complex task that falls und... | Reject | 3 | [
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],
"rating": "6: marginally above the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=tB7p0SM5TH | 2412.09968 | papers/tB7p0SM5TH.pdf | 707198cc4b4e9ccf36a0238d3a3f2ca62b0a5fe09b5767e57ec4428d1d4df89e | 1,295,848 | openreview | https://github.com/HaoranZ99/GraSP | HaoranZ99/GraSP | d7d89bf1197ec3fb8f4c9ab578ab4ebace757767 | repos/tB7p0SM5TH.zip | b597a61fdfa6fc6a6b97ae7b47a99dbe750f4b99084eaeb3eb4eefebb41a7d02 | 16,412 | 8 | {
".py": 7,
".sh": 1
} | 16 | {
"Python": 47638,
"Shell": 867
} | false | 2026-02-25T12:42:37 | {
"method": "exact_normalized_title",
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"pwc_url": "https://paperswithcode.com/paper/grasp-simple-yet-effective-graph-similarity"
} | {
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} | |
OYKIo3ySkxA | 2,023 | rejected | DIGEST: FAST AND COMMUNICATION EFFICIENT DECENTRALIZED LEARNING WITH LOCAL UPDATES | [
"Peyman Gholami",
"Hulya Seferoglu"
] | [
"~Peyman_Gholami1",
"~Hulya_Seferoglu1"
] | OpenReview API | Decentralized learning advocates the elimination of centralized parameter servers
(aggregation points) for potentially better utilization of underlying resources, de-
lay reduction, and resiliency against parameter server unavailability and catas-
trophic failures. Gossip based decentralized algorithms, where each node... | Reject | null | 3 | [
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"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=OYKIo3ySkxA | 2307.07652 | papers/OYKIo3ySkxA.pdf | 3518c5921b252f6f3b450ae28a83dd746017e2a794fdaadf63f0511f7b4a98bb | 1,272,206 | openreview | https://github.com/Anonymous404404/DigestCode | Anonymous404404/DigestCode | 9499a2d81eaf1380a823cdb73436af6f417bf161 | repos/OYKIo3ySkxA.zip | 8ebf4c976ce8d08dbb8928312017dbe24dba1eea86b253056a2cd2424bde237f | 23,552 | 7 | {
".py": 7
} | 21 | {
"Python": 41988
} | false | 2023-01-26T19:34:46 | {
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} | {
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r1lUl6NFDH | 2,020 | rejected | Mirror Descent View For Neural Network Quantization | [
"Thalaiyasingam Ajanthan",
"Kartik Gupta",
"Philip H. S. Torr",
"Richard Hartley",
"Puneet K. Dokania"
] | [
"thalaiyasingam.ajanthan@anu.edu.au",
"kartik.gupta@anu.edu.au",
"phst@robots.ox.ac.uk",
"richard.hartley@anu.edu.au",
"puneet@robots.ox.ac.uk"
] | OpenReview API | Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. NN quantization is usually formulated as a constrained optimization problem and optimized via a modified version of gradient descent. In this work, by inter... | Reject | null | 4 | [
{
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"rating": "3: Weak Reject",
"confidence": "",
"recommendation": "",
"soundness": "",
"presentation": "",
"contribution": "",
"correctness": "",
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"revi... | https://openreview.net/forum?id=r1lUl6NFDH | 1910.08237 | papers/r1lUl6NFDH.pdf | c8e50b0d3dc1dde1a52f8f4dd4dab3a037acf02fad32172b251e0edaf7ce4525 | 564,973 | openreview | https://github.com/kartikgupta-at-anu/md-bnn | kartikgupta-at-anu/md-bnn | b42d5aabd78b73b2baee0858b7f2b8ee72e36d6c | repos/r1lUl6NFDH.zip | bbe81658a01507d01fb7f3e7ebba485364d55da038f32cff708027df4f17772b | 49,542 | 30 | {
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} | 24 | {
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} | false | 2021-02-19T00:59:07 | {
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By41BjA9YQ | 2,019 | rejected | Laplacian Smoothing Gradient Descent | [
"Stanley J. Osher",
"Bao Wang",
"Penghang Yin",
"Xiyang Luo",
"Minh Pham",
"Alex T. Lin"
] | [
"sjo@math.ucla.edu",
"wangbaonj@gmail.com",
"yph@g.ucla.edu",
"xylmath@gmail.com",
"minhrose@ucla.edu",
"atlin@math.ucla.edu"
] | OpenReview API | We propose a class of very simple modifications of gradient descent and stochastic gradient descent. We show that when applied to a large variety of machine learning problems, ranging from softmax regression to deep neural nets, the proposed surrogates can dramatically reduce the variance and improve the generalization... | null | Reject | 3 | [
{
"id": "S1ehQaOKnm",
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],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soun... | https://openreview.net/forum?id=By41BjA9YQ | 1806.06317 | papers/By41BjA9YQ.pdf | ee79d1e9ddd55db98ff61e5f20c598b8ffe83d3ba0f44c3a48a09629784f58ac | 1,165,708 | openreview | https://github.com/BaoWangMath/LaplacianSmoothing-GradientDescent | BaoWangMath/LaplacianSmoothing-GradientDescent | ba394837a6857677c1e243a375f495be32da8a5e | repos/By41BjA9YQ.zip | 5264e98d4795aa28d5bab59df6cc05595d5eed7d1372c1daaf4d5d04c450fd08 | 28,493 | 15 | {
".py": 15
} | 50 | {
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} | false | 2019-05-26T18:29:37 | {
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} | {
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} |
HJSA_e1AW | 2,018 | rejected | Normalized Direction-preserving Adam | [
"Zijun Zhang",
"Lin Ma",
"Zongpeng Li",
"Chuan Wu"
] | [
"zijun.zhang@ucalgary.ca",
"linmawhu@gmail.com",
"zongpeng@ucalgary.ca",
"cwu@cs.hku.hk"
] | OpenReview API | Optimization algorithms for training deep models not only affects the convergence rate and stability of the training process, but are also highly related to the generalization performance of trained models. While adaptive algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic g... | Reject | null | 3 | [
{
"id": "S1-Kfe5lM",
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"ICLR.cc/2018/Conference/Paper115/AnonReviewer1"
],
"rating": "5: Marginally below acceptance threshold",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
"r... | https://openreview.net/forum?id=HJSA_e1AW | 1709.04546 | papers/HJSA_e1AW.pdf | a9e704f314545c18f8c31037845072a1b898f859041161991bab73471c4d3cda | 650,436 | openreview | https://github.com/zj10/ND-Adam | zj10/ND-Adam | b4a9d59b9c3607bbf734dc8c893fdbdce7bd1bef | repos/HJSA_e1AW.zip | 98ecfe34d94e0eab54ded44bf322641d1206e2d6dcf213eaa722f66bfa2ab467 | 19,208 | 9 | {
".py": 9
} | 26 | {
"Python": 51045
} | false | 2018-09-19T04:58:16 | {
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"pwc_url": "https://paperswithcode.com/paper/normalized-direction-preserving-adam"
} | {
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} |
LJ6AvummWu | 2,026 | rejected | SOReL and TOReL: Two Methods for Fully Offline Reinforcement Learning | [
"Mattie Fellows",
"Clarisse Wibault",
"Uljad Berdica",
"Johannes Forkel",
"Michael A Osborne",
"Jakob Nicolaus Foerster"
] | [
"~Mattie_Fellows1",
"~Clarisse_Wibault1",
"~Uljad_Berdica1",
"~Johannes_Forkel1",
"~Michael_A_Osborne1",
"~Jakob_Nicolaus_Foerster1"
] | OpenReview API | Sample efficiency remains a major obstacle for real world adoption of reinforcement learning (RL): success has been limited to settings where simulators provide access to essentially unlimited environment interactions, which in reality are typically costly or dangerous to obtain. Offline RL in principle offers a soluti... | Reject | 4 | [
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"rating": 4,
"soundness": 2,
"presentation": 3,
"contribution": 3,
"confidence": 4,
"summary": "This paper considers two salient problems with offline RL: (a) what offli... | https://openreview.net/forum?id=LJ6AvummWu | 2505.22442 | papers/LJ6AvummWu.pdf | 101a7e7b321d394a985c9b6d12e48597c5aff6718f7d07974400677c95105b82 | 6,541,472 | openreview | https://github.com/CWibault/sorel_torel | CWibault/sorel_torel | 8babfc8950e8acbdbf252b1e6cd3f34048c17793 | repos/LJ6AvummWu.zip | 3c9fe3661e799299cff2a9b26a5a4e6c2b8cba127db80381da91ee2359f64844 | 81,895 | 41 | {
".py": 37,
".sh": 4
} | 60 | {
"Python": 216922,
"Shell": 998
} | false | 2025-05-27T22:26:15 | {
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"pwc_url": "https://paperswithcode.com/paper/sorel-and-torel-two-methods-for-fully-offline"
} | {
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} | |
cWrqs2lwCJ | 2,025 | rejected | Thinking Forward and Backward: Effective Backward Planning with Large Language Models | [
"Allen Z. Ren",
"brian ichter",
"Anirudha Majumdar"
] | [
"~Allen_Z._Ren1",
"~brian_ichter1",
"~Anirudha_Majumdar1"
] | OpenReview API | Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities. Most prior work in this area has used LLMs to reason through steps from an initial to a goal state or criterion, thereby effectively reasoning in a forward direction. Nonetheless, many planning problems exhibit an inherent asymm... | Reject | 4 | [
{
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],
"rating": 3,
"soundness": 2,
"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "The authors propose a method that uses large language models (LLMs) for pl... | https://openreview.net/forum?id=cWrqs2lwCJ | 2411.01790 | papers/cWrqs2lwCJ.pdf | 4ee1a03edcc2ea1b74f12996f37a6e59ab01cbbb52cc4fde1e03f95ff03a6da9 | 4,620,505 | openreview | https://github.com/irom-princeton/llm-backward | irom-princeton/llm-backward | b215c6785a18803a4b116f06e6850a19cc7a6048 | repos/cWrqs2lwCJ.zip | bb0ad5cafa0bcf8812a3d139d30e81825a5104a49e0703a4b8ebe82c9f120f19 | 22,707 | 12 | {
".py": 12
} | 18 | {
"Python": 78294
} | false | 2024-11-05T03:32:56 | {
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} | {
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} | |
53kW6e1uNN | 2,024 | rejected | AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations | [
"Wei Wu",
"Chao Wang",
"Dazhong Shen",
"Chuan Qin",
"Hui Xiong"
] | [
"~Wei_Wu25",
"~Chao_Wang14",
"~Dazhong_Shen1",
"~Chuan_Qin1",
"~Hui_Xiong1"
] | OpenReview API | Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduc... | Reject | 4 | [
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],
"rating": "8: accept, good paper",
"soundness": "3 good",
"presentation": "4 excellent",
"contribution": "3 good",
"confidence": "5: You are absolutely certain about your ass... | https://openreview.net/forum?id=53kW6e1uNN | 2403.17416 | papers/53kW6e1uNN.pdf | f2c133df5a7f07873788c4cdf3ae3e1e2e8354091dca97c5e6ac53b1ed79e345 | 6,094,555 | openreview | https://github.com/U-rara/AFDGCF | U-rara/AFDGCF | 0eba7b6dbf7cfd2c1a0dcebd9e2f0742a68b89aa | repos/53kW6e1uNN.zip | 38919b617762d350bf5da4ffcd8222d0bf84bfcab807a169ba094aec4410d806 | 29,630 | 11 | {
".py": 11
} | 16 | {
"Python": 81015
} | false | 2024-01-23T13:47:58 | {
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"pwc_url": "https://paperswithcode.com/paper/afdgcf-adaptive-feature-de-correlation-graph"
} | {
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t-hNmA0cVSW | 2,023 | rejected | Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling | [
"Hui LIN",
"Zhiheng Ma",
"Rongrong Ji",
"Yaowei Wang",
"su zhou",
"Xiaopeng Hong"
] | [
"~Hui_LIN4",
"~Zhiheng_Ma1",
"~Rongrong_Ji5",
"~Yaowei_Wang1",
"~su_zhou1",
"~Xiaopeng_Hong4"
] | OpenReview API | This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled. We formulate the pixel-wise density value to regress as a probability distribution, instead of a single deterministic value, and utilize a dual-branch structure to model the corresponding discrete form of ... | Reject | null | 4 | [
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"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces... | https://openreview.net/forum?id=t-hNmA0cVSW | 2402.15297 | papers/t-hNmA0cVSW.pdf | a9f514d0bce69c0ecbdd86e9de62c489df551d4d369dc5cd0f5b309098caf099 | 1,719,675 | openreview | https://github.com/LoraLinH/Semi-supervised-Counting-via-Pixel-by-pixel-Density-Distribution-Modelling | LoraLinH/Semi-supervised-Counting-via-Pixel-by-pixel-Density-Distribution-Modelling | 66034010cc7134fa5265b282e606cdcb7beb3399 | repos/t-hNmA0cVSW.zip | 2ab266684d36250a645091676a1e02f61111bfd66817cb416102b131df813996 | 19,965 | 11 | {
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} | 21 | {
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} | false | 2025-04-02T11:55:14 | {
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"pwc_url": "https://paperswithcode.com/paper/semi-supervised-counting-via-pixel-by-pixel"
} | {
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gxk4-rVATDA | 2,022 | rejected | Bit-wise Training of Neural Network Weights | [
"Cristian Ivan"
] | [
"~Cristian_Ivan1"
] | OpenReview API | We propose an algorithm where the individual bits representing the weights of a neural network are learned. This method allows training weights with integer values on arbitrary bit-depths and naturally uncovers sparse networks, without additional constraints or regularization techniques. We show better results than the... | Reject | null | 4 | [
{
"id": "HmWwXJPxJXg",
"reviewer_signature": [
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],
"rating": "",
"confidence": "5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.",
"recommendat... | https://openreview.net/forum?id=gxk4-rVATDA | 2202.09571 | papers/gxk4-rVATDA.pdf | 525dfaab09c5bd0aaf95bff67a39eb0a9c58b57103475c37b2bf7086db9e04ed | 2,383,381 | openreview | https://github.com/iclr2022-2798/bit-wise-training | iclr2022-2798/bit-wise-training | 39fdc97caa080bd3b09ae7abcef75f79ca6dac5d | repos/gxk4-rVATDA.zip | 5131a7aabdd85cade759642bde287a67de69543b370984bf71e1e753f8197425 | 12,573 | 5 | {
".py": 5
} | 20 | {
"Python": 47611
} | false | 2021-10-11T20:45:43 | {
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"pwc_url": "https://paperswithcode.com/paper/bit-wise-training-of-neural-network-weights-1"
} | {
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ztMLindFLWR | 2,021 | rejected | Breaking the Expressive Bottlenecks of Graph Neural Networks | [
"Mingqi Yang",
"Yanming Shen",
"Heng Qi",
"Baocai Yin"
] | [
"~Mingqi_Yang1",
"shen@dlut.edu.cn",
"hengqi@dlut.edu.cn",
"~Baocai_Yin1"
] | OpenReview API | Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressiveness of graph neural networks (GNNs), showing that the neighborhood aggregation GNNs were at most as powerful as 1-WL test in distinguishing graph structures. There were also improvements proposed in analogy to $k$-WL test ($k... | Reject | null | 5 | [
{
"id": "q6gXaHXeIy",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper1702/AnonReviewer5"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"so... | https://openreview.net/forum?id=ztMLindFLWR | 2012.07219 | papers/ztMLindFLWR.pdf | 676865eab7cb0510e8c452632a31dfb4db77efee5a9020e0ca4ba0758ee19194 | 1,365,702 | openreview | https://github.com/qslim/epcb-gnns | qslim/epcb-gnns | 87bca5bb371ec15fe0fb68f7a93ae9933ef0e76a | repos/ztMLindFLWR.zip | a35d57fdb76fe8cf73bf1e332a2b5b0fd2ca66da11ef37c997592de85ba0f047 | 36,353 | 24 | {
".py": 19,
".sh": 5
} | 27 | {
"Python": 99910,
"Shell": 3373
} | false | 2022-06-21T08:29:30 | {
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H1xTup4KPr | 2,020 | rejected | Needles in Haystacks: On Classifying Tiny Objects in Large Images | [
"Nick Pawlowski",
"Suvrat Bhooshan",
"Nicolas Ballas",
"Francesco Ciompi",
"Ben Glocker",
"Michal Drozdzal"
] | [
"pawlowski.nick@gmail.com",
"sbh@fb.com",
"ballasn@fb.com",
"f.ciompi@gmail.com",
"b.glocker@imperial.ac.uk",
"mdrozdzal@fb.com"
] | OpenReview API | In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional Neural Networks (CNNs) for image classification were developed using biased datasets that contain large objects, in mostly central image posi... | Reject | null | 3 | [
{
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"rating": "1: Reject",
"confidence": "",
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"soundness": "",
"presentation": "",
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"review_te... | https://openreview.net/forum?id=H1xTup4KPr | 1908.06037 | papers/H1xTup4KPr.pdf | 7da3f4a4f9ca3f738bbf5e6132d49c609df7a82a032badf85147fc6afc45cc3a | 13,200,937 | openreview | https://github.com/facebookresearch/Needles-in-Haystacks | facebookresearch/Needles-in-Haystacks | bd08ae43b8174f9e5cda8763d99ba70a26a5b0c6 | repos/H1xTup4KPr.zip | c1803276f7adfe6b7477887cf4442a0fc16eaf26f99b9bf1e8c8e81943af993c | 32,871 | 17 | {
".py": 17
} | 25 | {
"Python": 58499
} | true | 2019-06-28T12:18:01 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/needles-in-haystacks-on-classifying-tiny"
} | {
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Bkf1tjR9KQ | 2,019 | rejected | DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search | [
"Guillaume Michel",
"Mohammed Amine Alaoui",
"Alice Lebois",
"Amal Feriani",
"Mehdi Felhi"
] | [
"guillaume.michel@netatmo.com",
"mohammed-amine.alaoui@netatmo.com",
"alice.lebois@netatmo.com",
"amal.feriani@netatmo.com",
"mehdi.felhi@netatmo.com"
] | OpenReview API | Automatic search of neural network architectures is a standing research topic. In addition to the fact that it presents a faster alternative to hand-designed architectures, it can improve their efficiency and for instance generate Convolutional Neural Networks (CNN) adapted for mobile devices. In this paper, we present... | null | Reject | 3 | [
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"rating": "4: Ok but not good enough - rejection",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundn... | https://openreview.net/forum?id=Bkf1tjR9KQ | 1902.01654 | papers/Bkf1tjR9KQ.pdf | 64c31a0ea1fa3bb2fdcfb0e2b8a41bee8f6e93f094ec74d634c03301deb5df7b | 376,334 | openreview | https://github.com/guillaume-michel/dvolver | guillaume-michel/dvolver | b1301c2790b91172fc3322b1836cb71e55afe7b9 | repos/Bkf1tjR9KQ.zip | 7760873ac19c77427b0984c6b4dd01022f3300df9dfe73ff128e2931e60291c8 | 65,891 | 29 | {
".py": 27,
".sh": 2
} | 54 | {
"Python": 198562,
"Shell": 4794
} | false | 2019-02-04T12:39:58 | {
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"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/dvolver-efficient-pareto-optimal-neural"
} | {
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SJmAXkgCb | 2,018 | rejected | DNN Feature Map Compression using Learned Representation over GF(2) | [
"Denis A. Gudovskiy",
"Alec Hodgkinson",
"Luca Rigazio"
] | [
"denis.gudovskiy@us.panasonic.com",
"alec.hodgkinson@us.panasonic.com",
"luca.rigazio@us.panasonic.com"
] | OpenReview API | In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field fo... | Reject | null | 3 | [
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"id": "BJ46Rwjez",
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"rating": "4: Ok but not good enough - rejection",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundne... | https://openreview.net/forum?id=SJmAXkgCb | 1808.05285 | papers/SJmAXkgCb.pdf | 55d99b9ba1df096bd61823c91e709a819b825bff577e7b60fc196caa026789d4 | 280,243 | openreview | https://github.com/gudovskiy/fmap_compression | gudovskiy/fmap_compression | 0e60097613b7fd7eac1d72775b202fc7a9bbbcea | repos/SJmAXkgCb.zip | eb371b7d36a9d4aa2b7ac6a2bc66806b25eb170273d7ba63fdda38dde43d6f23 | 75,728 | 17 | {
".cpp": 7,
".cu": 6,
".hpp": 3,
".cuh": 1
} | 29 | {
"C++": 72058,
"Cuda": 28024
} | false | 2018-11-21T01:40:02 | {
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"pwc_url": "https://paperswithcode.com/paper/dnn-feature-map-compression-using-learned"
} | {
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Am95bfE207 | 2,026 | rejected | From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning | [
"Hossein Zakerinia",
"Dorsa Ghobadi",
"Christoph H. Lampert"
] | [
"~Hossein_Zakerinia1",
"~Dorsa_Ghobadi2",
"~Christoph_H._Lampert6"
] | OpenReview API | Deep learning methods are known to generalize well from training to future data, even in an overparametrized regime, where they could easily overfit. One explanation for this phenomenon is that even when their ambient dimensionality, (i.e. the number of parameters) is large, the models’ intrinsic dimensionality is smal... | Reject | 5 | [
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"rating": 4,
"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "The paper proposes a parameter-sharing approach for multi-task learning b... | https://openreview.net/forum?id=Am95bfE207 | 2501.19067 | papers/Am95bfE207.pdf | afc7be806de1d88d2201446c496281181c0d0472e4f4299f9035659d7286d605 | 350,604 | openreview | https://github.com/hzakerinia/MTL | hzakerinia/MTL | b20ca886303c8cfe0a067422e653373c5c239c0a | repos/Am95bfE207.zip | a01c1af3089054a0e9a9933edc755f63dadf690ee359023d4041ab5bed4e8eff | 73,462 | 32 | {
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} | 60 | {
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} | false | 2025-05-14T12:33:35 | {
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} | |
fvo6q86NKG | 2,025 | rejected | CBF-LLM: Safe Control for LLM Alignment | [
"Yuya Miyaoka",
"Masaki Inoue"
] | [
"~Yuya_Miyaoka1",
"~Masaki_Inoue1"
] | OpenReview API | This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation.
The presented framework applies the CBF safety filter to the predicted token generated from the baseline LLM, to intervene in the generated te... | Reject | 5 | [
{
"id": "CjhgOnzJ9D",
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"ICLR.cc/2025/Conference/Submission8657/Reviewer_jp7H"
],
"rating": 1,
"soundness": 2,
"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "The paper studies controllable decoding in LLM generation, e.g., keeping t... | https://openreview.net/forum?id=fvo6q86NKG | 2408.15625 | papers/fvo6q86NKG.pdf | f1b3100272c76f672f0a93be47f34dc94fe7aefab5b3bb1c3597e8bb4b0dd4c9 | 486,425 | openreview | https://github.com/Mya-Mya/CBF-LLM | Mya-Mya/CBF-LLM | 27cc8388f0f7e6f463848c2ecc0ca7989fc8b579 | repos/fvo6q86NKG.zip | d84b0d6dd7e378e0029816e21fdaf39b5350d31504eedef9116ce29e21a5165d | 8,167 | 9 | {
".py": 9
} | 20 | {
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} | false | 2024-12-10T11:15:13 | {
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"pwc_url": "https://paperswithcode.com/paper/cbf-llm-safe-control-for-llm-alignment"
} | {
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} | |
mOTiVzTgF2 | 2,024 | rejected | ResiDual: Transformer with Dual Residual Connections | [
"Shufang Xie",
"Huishuai Zhang",
"Junliang Guo",
"Xu Tan",
"Jiang Bian",
"Hany Hassan Awadalla",
"Arul Menezes",
"Tao Qin",
"Rui Yan"
] | [
"~Shufang_Xie1",
"~Huishuai_Zhang3",
"~Junliang_Guo1",
"~Xu_Tan1",
"~Jiang_Bian1",
"~Hany_Hassan_Awadalla1",
"~Arul_Menezes1",
"~Tao_Qin1",
"~Rui_Yan2"
] | OpenReview API | Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance. However, the optimal way to implement residual connections in Transformer, which are essential for effective training, is still debated. Two widely used variants are the Post-Layer-Normalization (Post-L... | Reject | 5 | [
{
"id": "s6LAjaFYSq",
"reviewer_signature": [
"ICLR.cc/2024/Conference/Submission5656/Reviewer_q8he"
],
"rating": "3: reject, not good enough",
"soundness": "2 fair",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "3: You are fairly confident in your assessme... | https://openreview.net/forum?id=mOTiVzTgF2 | 2304.14802 | papers/mOTiVzTgF2.pdf | b74039d1cd2b681315278bc56f8d4dfc03d1617ebc4765548fc803e82aaa5b7b | 607,065 | openreview | https://github.com/microsoft/ResiDual | microsoft/ResiDual | 8682f7510be105a0caf5b98a021e712f44f90ace | repos/mOTiVzTgF2.zip | cec65c73f8e80c77f8450dcd56aefe54692dbfcb4702ca6c61522f17e9994aa7 | 15,699 | 3 | {
".py": 3
} | 17 | {
"Python": 20837
} | true | 2023-08-18T18:23:56 | {
"method": "exact_normalized_title",
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"pwc_url": "https://paperswithcode.com/paper/residual-transformer-with-dual-residual"
} | {
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} | |
4nrZXPFN1c4 | 2,023 | rejected | Energy Transformer | [
"Benjamin Hoover",
"Yuchen Liang",
"Bao Pham",
"Rameswar Panda",
"Hendrik Strobelt",
"Duen Horng Chau",
"Mohammed J Zaki",
"Dmitry Krotov"
] | [
"~Benjamin_Hoover1",
"~Yuchen_Liang2",
"~Bao_Pham1",
"~Rameswar_Panda1",
"~Hendrik_Strobelt1",
"~Duen_Horng_Chau1",
"~Mohammed_J_Zaki1",
"~Dmitry_Krotov2"
] | OpenReview API | Transformers have become the de facto models of choice in machine learning, typically leading to impressive performance on many applications. At the same time, the architectural development in the transformer world is mostly driven by empirical findings, and the theoretical understanding of their architectural buildi... | Reject | null | 5 | [
{
"id": "TghgcpJcrYw",
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"ICLR.cc/2023/Conference/Paper2834/Reviewer_iuLg"
],
"rating": "",
"confidence": "2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar w... | https://openreview.net/forum?id=4nrZXPFN1c4 | 2302.07253 | papers/4nrZXPFN1c4.pdf | 4583760c02fa5b8a3975c53b295601c92b33a05e9c07e98fad9260c38a7d17cc | 19,682,945 | openreview | https://github.com/zhuergou/Energy-Transformer-for-Graph-Anomaly-Detection | zhuergou/Energy-Transformer-for-Graph-Anomaly-Detection | 958cb1db8a2a1eae106ba2a179c952e38cab179e | repos/4nrZXPFN1c4.zip | 9ee7efb5b8d5824211794a9fe2c79c43429f044c2db429d225a56900cd9fd0bf | 19,192 | 9 | {
".py": 9
} | 25 | {
"Python": 57204
} | false | 2023-05-21T03:57:58 | {
"method": "exact_normalized_title",
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"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/energy-transformer"
} | {
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} |
IEsx-jwFk3g | 2,022 | rejected | Deep Representations for Time-varying Brain Datasets | [
"Sikun Lin",
"Shuyun Tang",
"Ambuj Singh"
] | [
"~Sikun_Lin1",
"~Shuyun_Tang1",
"~Ambuj_Singh1"
] | OpenReview API | Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) cannot capture the whole picture of underlying brain activities, and previous works lack the ability to... | Reject | null | 4 | [
{
"id": "pimSlMRCZNk",
"reviewer_signature": [
"ICLR.cc/2022/Conference/Paper2184/Reviewer_Cuxq"
],
"rating": "",
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=IEsx-jwFk3g | 2205.11648 | papers/IEsx-jwFk3g.pdf | e45af310eaeeb14566f45d6e65cfa34cac9477d05d84831e4c79dc7f0139c109 | 34,179,969 | openreview | https://github.com/sklin93/ReBraiD | sklin93/ReBraiD | 7fbecd68e0fba5b86670916441dc509098417b42 | repos/IEsx-jwFk3g.zip | f3a2ddf49f894acec99d6785488dae4eaa60e6033cbdd2a8cdf4821190f8fbc5 | 18,930 | 5 | {
".py": 5
} | 22 | {
"Python": 77048
} | false | 2022-08-16T19:31:48 | {
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"pwc_url": "https://paperswithcode.com/paper/deep-representations-for-time-varying-brain-1"
} | {
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} |
RGeQOjc58d | 2,021 | rejected | Improved Gradient based Adversarial Attacks for Quantized Networks | [
"Kartik Gupta",
"Thalaiyasingam Ajanthan"
] | [
"~Kartik_Gupta2",
"~Thalaiyasingam_Ajanthan1"
] | OpenReview API | Neural network quantization has become increasingly popular due to efficient memory consumption and faster computation resulting from bitwise operations on the quantized networks. Even though they exhibit excellent generalization capabilities, their robustness properties are not well-understood. In this work, we system... | Reject | null | 5 | [
{
"id": "RXMZIKm0_eP",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper813/AnonReviewer5"
],
"rating": "7: Good paper, accept",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundness": "",
"... | https://openreview.net/forum?id=RGeQOjc58d | 2003.13511 | papers/RGeQOjc58d.pdf | c13eafc3dddfaebeae3649727ec54e41cb61d874d173bc4ac9a31dc71090386e | 455,817 | openreview | https://github.com/kartikgupta-at-anu/attack-bnn | kartikgupta-at-anu/attack-bnn | 79c60f64b9ced3084571c805404876475f2bf1bd | repos/RGeQOjc58d.zip | 66abc5e1ddba9997967ba2a07f88f849a1510a09070e72843bbb04b229823d3b | 40,718 | 26 | {
".py": 20,
".sh": 6
} | 28 | {
"Python": 111733,
"Shell": 30760
} | false | 2022-05-06T13:58:39 | {
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"pwc_url": "https://paperswithcode.com/paper/improved-gradient-based-adversarial-attacks"
} | {
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} |
SylUzpNFDS | 2,020 | rejected | SoftLoc: Robust Temporal Localization under Label Misalignment | [
"Julien Schroeter",
"Kirill Sidorov",
"Dave Marshall"
] | [
"schroeterj1@cardiff.ac.uk",
"sidorovk@cardiff.ac.uk",
"marshallad@cardiff.ac.uk"
] | OpenReview API | This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data. We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learn... | Reject | null | 3 | [
{
"id": "r1e8STrIqH",
"reviewer_signature": [
"ICLR.cc/2020/Conference/Paper414/AnonReviewer1"
],
"rating": "3: Weak Reject",
"confidence": "",
"recommendation": "",
"soundness": "",
"presentation": "",
"contribution": "",
"correctness": "",
"summary": "",
"revi... | https://openreview.net/forum?id=SylUzpNFDS | null | papers/SylUzpNFDS.pdf | e29061a3b055fb66bfb6dbc3dcc1cad4df2748cc151d5544853debbf84cdc2c8 | 1,232,214 | openreview | https://github.com/SoftLocNIPS/submission | SoftLocNIPS/submission | c5a5106c68070cd8fb037f2940e885132e58f463 | repos/SylUzpNFDS.zip | db19d79331b9dbeb14a099eab4f7402570adf7b023b482f76c5d02378f130f9d | 28,830 | 12 | {
".py": 11,
".sh": 1
} | 25 | {
"Python": 75271,
"Shell": 324
} | false | 2019-08-12T13:18:36 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/softloc-robust-temporal-localization-under"
} | {
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"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
rkxjnjA5KQ | 2,019 | rejected | Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation | [
"Shani Gamrian",
"Yoav Goldberg"
] | [
"gamrianshani@gmail.com",
"yoav.goldberg@gmail.com"
] | OpenReview API | Deep Reinforcement Learning has managed to achieve state-of-the-art results in learning control policies directly from raw pixels. However, despite its remarkable success, it fails to generalize, a fundamental component required in a stable Artificial Intelligence system. Using the Atari game Breakout, we demonstrate t... | null | Reject | 3 | [
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],
"rating": "7: Good paper, accept",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"presentation": "",
... | https://openreview.net/forum?id=rkxjnjA5KQ | 1806.07377 | papers/rkxjnjA5KQ.pdf | ddf056ac04908711dd3f5205bc275217b564ac6218eaecd8c2738f4d56b60b5b | 928,773 | openreview | https://github.com/ShaniGam/RL-GAN | ShaniGam/RL-GAN | 6c4e5f95826b2b99a893e66380050ff38b1d0cf5 | repos/rkxjnjA5KQ.zip | 4c66f9ec7d6373bd2d651ab858d9607e6a180170597f8c9e34d39b2270e559c8 | 53,667 | 24 | {
".py": 24
} | 55 | {
"Python": 147451
} | false | 2020-03-22T17:20:07 | {
"method": "exact_normalized_title",
"pwc_official": true,
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"pwc_url": "https://paperswithcode.com/paper/transfer-learning-for-related-reinforcement"
} | {
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} |
SyL9u-WA- | 2,018 | rejected | Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization | [
"Jiong Zhang",
"Qi Lei",
"Inderjit S. Dhillon"
] | [
"zhangjiong724@utexas.edu",
"leiqi@ices.utexas.edu",
"inderjit@cs.utexas.edu"
] | OpenReview API | Vanishing and exploding gradients are two of the main obstacles in training deep neural networks, especially in capturing long range dependencies in recurrent neural networks (RNNs). In this paper, we present an efficient parametrization of the transition matrix of an RNN that allows us to stabilize the gradients that ... | Reject | null | 3 | [
{
"id": "Syi9ojdgf",
"reviewer_signature": [
"ICLR.cc/2018/Conference/Paper699/AnonReviewer2"
],
"rating": "7: Good paper, accept",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundness": "",
"pr... | https://openreview.net/forum?id=SyL9u-WA- | 1803.09327 | papers/SyL9u-WA-.pdf | fcca7d93b35e55d0b202f55be69405cfd6d0b95252b4207da358946bedc07260 | 2,266,663 | openreview | https://github.com/zhangjiong724/spectral-RNN | zhangjiong724/spectral-RNN | c15407a84de5a0fe9244ccbde4de8b351ae18162 | repos/SyL9u-WA-.zip | 81c35bb8b80e387262f17cecd33f010be4ad9e60a1ffe014440fda53b1029dc7 | 45,387 | 22 | {
".cc": 12,
".py": 10
} | 33 | {
"C++": 73502,
"Python": 52179,
"Makefile": 4469
} | false | 2018-06-05T22:55:24 | {
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"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/stabilizing-gradients-for-deep-neural"
} | {
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uDmxJ6133n | 2,026 | rejected | TOWARD MEMORY-AIDED WORLD MODELS: BENCHMARKING VIA SPATIAL CONSISTENCY | [
"Kewei Lian",
"Shaofei Cai",
"Yilun Du",
"Yitao Liang"
] | [
"~Kewei_Lian1",
"~Shaofei_Cai2",
"~Yilun_Du1",
"~Yitao_Liang1"
] | OpenReview API | The ability to simulate the world in a spatially consistent manner is a crucial requirements for effective world models. Such a model enables high-quality visual generation, and also ensures the reliability of world models for downstream tasks such as simulation and planning. Designing a memory module is a crucial comp... | Reject | 4 | [
{
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"reviewer_signature": [
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],
"rating": 4,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "The paper focuses on evaluating the spatial consistency capability of wor... | https://openreview.net/forum?id=uDmxJ6133n | 2505.22976 | papers/uDmxJ6133n.pdf | 41b5769420a7277901ee5195af95f916da274dafbbe6f985c8a651bfa0415920 | 18,188,318 | openreview | https://github.com/Kevin-lkw/LoopNav | Kevin-lkw/LoopNav | d60a052ecf7dd8f7ff176e34f5065ccb0d9cdfc8 | repos/uDmxJ6133n.zip | 8eb651772287b4f9c0fa4ec70bcbbb20f07990a33e334b1d4b816d1271ac6c82 | 61,598 | 17 | {
".py": 14,
".js": 3
} | 62 | {
"Python": 157556,
"JavaScript": 17112
} | false | 2026-05-08T03:56:56 | {
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} | {
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o2uHg0Skil | 2,025 | rejected | RL, but don't do anything I wouldn't do | [
"Michael K. Cohen",
"Marcus Hutter",
"Yoshua Bengio",
"Stuart Russell"
] | [
"~Michael_K._Cohen1",
"~Marcus_Hutter1",
"~Yoshua_Bengio1",
"~Stuart_Russell1"
] | OpenReview API | In reinforcement learning, if the agent's reward differs from the designers' true utility, even only rarely, the state distribution resulting from the agent's policy can be very bad, in theory and in practice. When RL policies would devolve into undesired behavior, a common countermeasure is KL regularization to a trus... | Reject | 4 | [
{
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"reviewer_signature": [
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"rating": 8,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 2,
"summary": "The paper investigates the effectiveness of KL regularization as a safety... | https://openreview.net/forum?id=o2uHg0Skil | 2410.06213 | papers/o2uHg0Skil.pdf | 0a6fdb60e3ba466a31a1978c9b7289c81868236a42cf42fcb9339bfbc2bccf81 | 1,964,952 | openreview | https://github.com/mkc1000/kl-fixed-mixture | mkc1000/kl-fixed-mixture | 494011e784f97b9501584a9289c0c8428b1dba64 | repos/o2uHg0Skil.zip | c845cfdbde0394e436d6de3a211a84d8b241a566717862834d831f3cc151a782 | 5,717 | 3 | {
".py": 3
} | 22 | {
"Python": 6995
} | false | 2024-10-11T05:58:05 | {
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} | {
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ZA9XUTseA9 | 2,024 | rejected | On the Implicit Bias of Adam | [
"Matias D. Cattaneo",
"Jason Matthew Klusowski",
"Boris Shigida"
] | [
"~Matias_D._Cattaneo1",
"~Jason_Matthew_Klusowski1",
"~Boris_Shigida1"
] | OpenReview API | In previous literature, backward error analysis was used to find ordinary differential equations (ODEs) approximating the gradient descent trajectory. It was found that finite step sizes implicitly regularize solutions because terms appearing in the ODEs penalize the two-norm of the loss gradients. We prove that the ex... | Reject | 4 | [
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"rating": "6: marginally above the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "3: You are fairly confiden... | https://openreview.net/forum?id=ZA9XUTseA9 | 2309.00079 | papers/ZA9XUTseA9.pdf | cd10ecbd46a1d312d3e340bca44707eb5e40480e65b84af53a2c48e1a5954bf2 | 629,521 | openreview | https://github.com/borshigida/implicit-bias-of-adam | borshigida/implicit-bias-of-adam | e25ea9a22085331bc7c2ef7b98752ffc8cf1ac5c | repos/ZA9XUTseA9.zip | 18b6ad2865c7045d94589be466a317f049c615549dcc67a4829b20f3f51e1b73 | 12,609 | 5 | {
".py": 5
} | 18 | {
"Python": 32552
} | false | 2024-07-29T13:01:08 | {
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"pwc_url": "https://paperswithcode.com/paper/on-the-implicit-bias-of-adam"
} | {
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hTCBqt7pgxf | 2,023 | rejected | Efficient block contrastive learning via parameter-free meta-node approximation | [
"Gayan K Kulatilleke",
"Marius Portmann",
"Shekhar S. Chandra"
] | [
"~Gayan_K_Kulatilleke1",
"~Marius_Portmann1",
"~Shekhar_S._Chandra1"
] | OpenReview API | Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative samples which is unscalable and computationally prohibitive with a quadratic time complexity. Sub-sampling is not optimal and incorrect neg... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=hTCBqt7pgxf | 2209.14067 | papers/hTCBqt7pgxf.pdf | 1faa3aa876f62b796fbe66fb364b6c80dddda2ebac6d7be7e6616e7db8fe4563 | 4,049,478 | openreview | https://github.com/gayanku/PAMC | gayanku/PAMC | f33a8b347b23945194f1425227243fe6b8745ffc | repos/hTCBqt7pgxf.zip | 277b9d70042e5802907bd4703d261c6b737c1422c49272547198123618e364c1 | 13,311 | 5 | {
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} | 28 | {
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} | false | 2022-09-29T00:46:41 | {
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gccdzDu5Ur | 2,022 | rejected | Combining Diverse Feature Priors | [
"Saachi Jain",
"Dimitris Tsipras",
"Aleksander Madry"
] | [
"~Saachi_Jain1",
"~Dimitris_Tsipras1",
"~Aleksander_Madry1"
] | OpenReview API | To improve model generalization, model designers often restrict the features that their models use, either implicitly or explicitly. In this work, we explore the design space of leveraging such feature priors by viewing them as distinct perspectives on the data. Specifically, we find that models trained with diverse se... | Reject | null | 4 | [
{
"id": "m7MjLVvGt7R",
"reviewer_signature": [
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],
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=gccdzDu5Ur | 2110.08220 | papers/gccdzDu5Ur.pdf | 4907d8e26920118f3ea6199aa60ee36ad71a3609225cfe748fe0ef4469163a54 | 3,702,503 | openreview | https://github.com/MadryLab/copriors | MadryLab/copriors | da5146bc3d02e7cfe3e48cde70f0d3b8eac536de | repos/gccdzDu5Ur.zip | 32dc2a2c5a37aeb7c6df414973f2c0f9ef88eb60a274ff958919f3ac549a857c | 24,795 | 12 | {
".py": 12
} | 25 | {
"Python": 67395
} | false | 2021-10-18T01:35:53 | {
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} | {
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} |
a5KvtsZ14ev | 2,021 | rejected | SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks | [
"Bahare Fatemi",
"Seyed Mehran Kazemi",
"Layla El Asri"
] | [
"~Bahare_Fatemi1",
"~Seyed_Mehran_Kazemi1",
"~Layla_El_Asri2"
] | OpenReview API | Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this problem is to infer the latent structure and then apply a GNN to the inferred graph. Unfortunately, the space of possible graph structures gro... | Reject | null | 5 | [
{
"id": "lPpVONlB-V",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper904/AnonReviewer5"
],
"rating": "5: Marginally below acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"pr... | https://openreview.net/forum?id=a5KvtsZ14ev | 2102.05034 | papers/a5KvtsZ14ev.pdf | bb9e2f129bf24419b4ead3ef650b6c585b0bcee101cc9fefd5558f2bc3ffce93 | 634,660 | openreview | https://github.com/BorealisAI/SLAPS-GNN | BorealisAI/SLAPS-GNN | 489481fd3fa1ba0be6b1d42b40acec8b8858b7ec | repos/a5KvtsZ14ev.zip | e11aeddc36869c9f631c3b1f4479aea731b9754a62bb65295b5934113067dd5d | 22,056 | 7 | {
".py": 7
} | 29 | {
"Python": 41538
} | false | 2021-10-25T22:12:41 | {
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} | {
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} |
rkxWpCNKvS | 2,020 | rejected | Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing | [
"Philip May"
] | [
"eniak.info@gmail.com"
] | OpenReview API | Image augmentation is a widely used technique to improve the performance of convolutional neural networks (CNNs). In common image shifting, cropping, flipping, shearing and rotating are used for augmentation. But there are more advanced techniques like Cutout and SamplePairing.
In this work we present two improvements... | Reject | null | 3 | [
{
"id": "SJgZAoMH9S",
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"ICLR.cc/2020/Conference/Paper1382/AnonReviewer1"
],
"rating": "1: Reject",
"confidence": "",
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"soundness": "",
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"contribution": "",
"correctness": "",
"summary": "",
"review_t... | https://openreview.net/forum?id=rkxWpCNKvS | 1909.00390 | papers/rkxWpCNKvS.pdf | ff24017a0a807d407b345a9fc932691e5194fdc0fb7eb2437e3b12665362a49a | 146,220 | openreview | https://github.com/t-systems-on-site-services-gmbh/coocop | t-systems-on-site-services-gmbh/coocop | 95f844404e22a99cc93a058fa2bd085685dc88c7 | repos/rkxWpCNKvS.zip | 41be181c2fc2c6ddd2f1d1305314665e4518ae3ab8f12268748bb888e7980987 | 6,027 | 5 | {
".py": 4,
".sh": 1
} | 25 | {
"Python": 9552,
"Shell": 201
} | false | 2019-10-11T19:13:12 | {
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} | {
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} |
SJNRHiAcYX | 2,019 | rejected | Boosting Trust Region Policy Optimization by Normalizing flows Policy | [
"Yunhao Tang",
"Shipra Agrawal"
] | [
"yt2541@columbia.edu",
"sa3305@columbia.edu"
] | OpenReview API | We propose to improve trust region policy search with normalizing flows policy. We illustrate that when the trust region is constructed by KL divergence constraint, normalizing flows policy can generate samples far from the 'center' of the previous policy iterate, which potentially enables better exploration and helps ... | null | Reject | 3 | [
{
"id": "rkeZNVXihX",
"reviewer_signature": [
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],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"sou... | https://openreview.net/forum?id=SJNRHiAcYX | 1809.10326 | papers/SJNRHiAcYX.pdf | 91db1cb255b3600befff0f4db9014ede4425171c84b729f9bcd92e7dc5065c87 | 4,355,503 | openreview | https://github.com/robintyh1/onpolicybaselines | robintyh1/onpolicybaselines | 58d401622b38a9127437cf48b1b62b27028d694a | repos/SJNRHiAcYX.zip | c421d56b8f02615e28b2c5eafb81d3ac91f10049375defedaa9c3da8a85b1e19 | 138,276 | 61 | {
".py": 61
} | 56 | {
"Python": 370267
} | false | 2020-04-03T18:54:27 | {
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} | {
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} |
Hk2MHt-3- | 2,018 | rejected | Coupled Ensembles of Neural Networks | [
"Anuvabh Dutt",
"Denis Pellerin",
"Georges Quénot"
] | [
"anuvabh.dutt@univ-grenoble-alpes.fr",
"denis.pellerin@gipsa-lab.grenoble-inp.fr",
"georges.quenot@imag.fr"
] | OpenReview API | We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an effici... | Reject | null | 3 | [
{
"id": "Hk8Nwx9xf",
"reviewer_signature": [
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],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundn... | https://openreview.net/forum?id=Hk2MHt-3- | 1709.06053 | papers/Hk2MHt-3-.pdf | e231be8aef025a2c6b1fdec34ece99bb2749ce5048791eb7174ed2e86fd6fe15 | 987,876 | openreview | https://github.com/vabh/coupled_ensembles | vabh/coupled_ensembles | c4ced6a13189e6f8b5420917509c3873e5ed6fb1 | repos/Hk2MHt-3-.zip | 2853f7669f0426629bf49825eec6639330c4c1743066495371c117bf7522cf00 | 27,692 | 10 | {
".py": 9,
".sh": 1
} | 35 | {
"Python": 46710,
"Shell": 761
} | false | 2019-02-25T12:59:07 | {
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} | {
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} |
ZkiVWvWwic | 2,026 | rejected | Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI | [
"Marlene Careil",
"Yohann Benchetrit",
"Jean-Remi King"
] | [
"~Marlene_Careil1",
"~Yohann_Benchetrit1",
"~Jean-Remi_King1"
] | OpenReview API | Brain-to-image decoding has been recently propelled by the progress in generative AI models and the availability of large ultra-high field functional Magnetic Resonance Imaging (fMRI). However, current approaches depend on complicated multi-stage pipelines and preprocessing steps that typically collapse the temporal di... | Reject | 4 | [
{
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"ICLR.cc/2026/Conference/Submission24883/Reviewer_LP21"
],
"rating": 4,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper introduces Dynadiff, a novel brain-to-image decoding model des... | https://openreview.net/forum?id=ZkiVWvWwic | 2505.14556 | papers/ZkiVWvWwic.pdf | b062985363f375d3641bfe3d25cfeb9abbc93dfa97de654d5c6509b1a4f02cf9 | 16,270,608 | openreview | https://github.com/facebookresearch/dynadiff | facebookresearch/dynadiff | b8d1f84a054826c2cea28a44beb2aac5a1839b8c | repos/ZkiVWvWwic.zip | 7e328e7c2a86be382d2cdb3c929d8cbc160377d4e65a1c519c930abde59973d4 | 71,422 | 16 | {
".py": 13,
".sh": 3
} | 65 | {
"Python": 95810,
"Shell": 2753
} | false | 2025-05-13T21:27:55 | {
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} | |
N5ID99rsUq | 2,024 | rejected | Stability and Generalization in Free Adversarial Training | [
"Xiwei Cheng",
"Kexin Fu",
"Farzan Farnia"
] | [
"~Xiwei_Cheng2",
"~Kexin_Fu2",
"~Farzan_Farnia1"
] | OpenReview API | While adversarial training methods have resulted in significant improvements in the deep neural nets' robustness against norm-bounded adversarial perturbations, their generalization performance from training samples to test data has been shown to be considerably worse than standard empirical risk minimization methods. ... | Reject | 4 | [
{
"id": "fI0C2keEQX",
"reviewer_signature": [
"ICLR.cc/2024/Conference/Submission4972/Reviewer_iGFe"
],
"rating": "6: marginally above the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=N5ID99rsUq | 2404.08980 | papers/N5ID99rsUq.pdf | 245600e9c70dccdcdf487b8ce159d9a0b14950e2e5725971c34cf582e28acf86 | 1,917,096 | openreview | https://github.com/Xiwei-Cheng/Stability_FreeAT | Xiwei-Cheng/Stability_FreeAT | aecc74df99c4874a23d1e3a1b6a7c84b682604a8 | repos/N5ID99rsUq.zip | 95d6bea8379d5e0b920490bd1414b8e62b8befe85c9c9e0d8b40aee18ba96af7 | 17,848 | 7 | {
".py": 7
} | 19 | {
"Python": 67532
} | false | 2024-04-08T09:28:46 | {
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"pwc_url": "https://paperswithcode.com/paper/stability-and-generalization-in-free"
} | {
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} | |
gLl0fZQo6Vu | 2,023 | rejected | Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information | [
"Riashat Islam",
"Manan Tomar",
"Alex Lamb",
"Hongyu Zang",
"Yonathan Efroni",
"Dipendra Misra",
"Xin Li",
"Harm van Seijen",
"Remi Tachet des Combes",
"John Langford"
] | [
"~Riashat_Islam1",
"~Manan_Tomar1",
"~Alex_Lamb1",
"~Hongyu_Zang1",
"~Yonathan_Efroni2",
"~Dipendra_Misra1",
"~Xin_Li31",
"~Harm_van_Seijen1",
"~Remi_Tachet_des_Combes1",
"~John_Langford1"
] | OpenReview API | Learning to control an agent from data collected offline in a rich pixel-based visual observation space is vital for real-world applications of reinforcement learning (RL). A major challenge in this setting is the presence of input information that is hard to model and irrelevant to controlling the agent. This problem ... | Reject | null | 5 | [
{
"id": "VeTKGSBVJ74",
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],
"rating": "",
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=gLl0fZQo6Vu | 2211.00164 | papers/gLl0fZQo6Vu.pdf | f38065170ab60adab869938b5b866e802caaafefa4b1e60722c8486a605e63db | 18,339,013 | openreview | https://github.com/manantomar/agent-centric-representations | manantomar/agent-centric-representations | f4622fadcb1fa2b6724dbab21ee62d1ac250bfe4 | repos/gLl0fZQo6Vu.zip | aea44ce95fee289f1a7a9434acae24387e48459e73863ead03950895890a7926 | 36,760 | 9 | {
".py": 9
} | 28 | {
"Python": 86139
} | false | 2023-08-14T01:00:34 | {
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MDT30TEtaVY | 2,022 | rejected | Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets | [
"Lily H Zhang",
"Veronica Tozzo",
"John M. Higgins",
"Rajesh Ranganath"
] | [
"~Lily_H_Zhang1",
"~Veronica_Tozzo2",
"~John_M._Higgins1",
"~Rajesh_Ranganath2"
] | OpenReview API | Permutation invariant neural networks are a promising tool for predictive modeling of set data. We show, however, that existing architectures struggle to perform well when they are deep. In this work, we address this issue for the two most widely used permutation invariant networks, Deep Sets and its transformer analog... | Reject | null | 4 | [
{
"id": "q850lD4eEI",
"reviewer_signature": [
"ICLR.cc/2022/Conference/Paper3756/Reviewer_Qf1Y"
],
"rating": "",
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=MDT30TEtaVY | 2206.11925 | papers/MDT30TEtaVY.pdf | e3a789479d8691761dcb0d4e57be72c22934034e3f297dabafceabc6e904ea9e | 461,720 | openreview | https://github.com/rajesh-lab/deep_permutation_invariant | rajesh-lab/deep_permutation_invariant | 7d25da12329d3d89a69c2f5333f94234248f01a5 | repos/MDT30TEtaVY.zip | a9e89578b413ce3fbe4bbac92580dd6b282418379548c6def7601e9949c456dc | 35,988 | 23 | {
".py": 23
} | 25 | {
"Python": 98456
} | false | 2022-10-11T20:48:51 | {
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} | {
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jpm1AfJucwt | 2,021 | rejected | Revisiting Loss Modelling for Unstructured Pruning | [
"César Laurent",
"Camille Ballas",
"Thomas George",
"Pascal Vincent",
"Nicolas Ballas"
] | [
"~César_Laurent1",
"~Camille_Ballas1",
"~Thomas_George2",
"~Pascal_Vincent1",
"~Nicolas_Ballas1"
] | OpenReview API | By removing parameters from deep neural networks, unstructured pruning methods aim at cutting down memory footprint and computational cost, while maintaining prediction accuracy. In order to tackle this otherwise intractable problem, many of these methods model the loss landscape using first or second order Taylor expa... | Reject | null | 4 | [
{
"id": "d1QycbMMAq4",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper432/AnonReviewer5"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
... | https://openreview.net/forum?id=jpm1AfJucwt | 2006.12279 | papers/jpm1AfJucwt.pdf | fb73c1aabdd4e9dcc51ba19a09b9c9c23079b20ba77f0f70a24a0135ef7a81d2 | 1,425,439 | openreview | https://github.com/Thrandis/loss-models-pruning | Thrandis/loss-models-pruning | b784b84cd2494e59673849dfd3b3e45e996a7e7a | repos/jpm1AfJucwt.zip | b474a4e926a41e698873d8c0deb6a703d8a079f61daaa2c59588c02954d86286 | 14,683 | 8 | {
".py": 8
} | 29 | {
"Python": 36312,
"Dockerfile": 194
} | false | 2020-09-17T19:48:09 | {
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} | {
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rJggX0EKwS | 2,020 | rejected | The Benefits of Over-parameterization at Initialization in Deep ReLU Networks | [
"Devansh Arpit",
"Yoshua Bengio"
] | [
"devansharpit@gmail.com",
"yoshua.bengio@mila.quebec"
] | OpenReview API | It has been noted in existing literature that over-parameterization in ReLU networks generally improves performance. While there could be several factors involved behind this, we prove some desirable theoretical properties at initialization which may be enjoyed by ReLU networks. Specifically, it is known that He initia... | Reject | null | 4 | [
{
"id": "SkxEBvuUcS",
"reviewer_signature": [
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"rating": "3: Weak Reject",
"confidence": "",
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"rev... | https://openreview.net/forum?id=rJggX0EKwS | 1901.03611 | papers/rJggX0EKwS.pdf | 7fb0a3faf914f6f556351d6d4aa8222e6c0053195d847f89ae11fcd60cdb4d8f | 622,695 | openreview | https://github.com/devansharpit/overparametrization_benefits | devansharpit/overparametrization_benefits | 9711531e87e51712eca8bb625e1a801534ef8676 | repos/rJggX0EKwS.zip | c324a1c08ac36e17ffcfb8832943bc1250ace9be4e36553d524828f93d9a6921 | 5,091 | 3 | {
".py": 3
} | 28 | {
"Python": 6016
} | false | 2019-08-20T02:44:58 | {
"method": "exact_normalized_title",
"pwc_official": true,
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"pwc_url": "https://paperswithcode.com/paper/the-benefits-of-over-parameterization-at"
} | {
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HJl0jiRqtX | 2,019 | rejected | EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE | [
"Chao Ma",
"Sebastian Tschiatschek",
"Konstantina Palla",
"Jose Miguel Hernandez Lobato",
"Sebastian Nowozin",
"Cheng Zhang"
] | [
"cm905@cam.ac.uk",
"sebastian.tschiatschek@microsoft.com",
"konstantina.palla@microsoft.com",
"jmh233@cam.ac.uk",
"sebastian.nowozin@microsoft.com",
"cheng.zhang@microsoft.com"
] | OpenReview API | Making decisions requires information relevant to the task at hand. Many real-life decision-making situations allow acquiring further relevant information at a specific cost. For example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans ... | null | Reject | 3 | [
{
"id": "HJxgYLy-pX",
"reviewer_signature": [
"ICLR.cc/2019/Conference/Paper666/AnonReviewer1"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central pa... | https://openreview.net/forum?id=HJl0jiRqtX | 1809.11142 | papers/HJl0jiRqtX.pdf | 4d9e5643c6d5f5bc86c5b9094b9a748e53f881cb5b97ede8a555e1fcd5ccfee3 | 3,555,859 | openreview | https://github.com/microsoft/EDDI | microsoft/EDDI | 835f39e13371a9727b8946e6fa5dcb21eaf28e14 | repos/HJl0jiRqtX.zip | bff214ea9fdd435670872fc0a62c9551552fa7bea831480501a68460ca0d9a99 | 38,451 | 7 | {
".py": 7
} | 82 | {
"Python": 68526
} | true | 2023-06-12T18:56:30 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/eddi-efficient-dynamic-discovery-of-high"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
SJzMATlAZ | 2,018 | rejected | Deep Continuous Clustering | [
"Sohil Atul Shah",
"Vladlen Koltun"
] | [
"sohilas@umd.edu",
"vkoltun@gmail.com"
] | OpenReview API | Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The data is embedded into a lower-dimensional space by a deep autoencoder. The autoenco... | Reject | null | 3 | [
{
"id": "H1ySNZVgf",
"reviewer_signature": [
"ICLR.cc/2018/Conference/Paper436/AnonReviewer3"
],
"rating": "3: Clear rejection",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
"recommendation": "",
... | https://openreview.net/forum?id=SJzMATlAZ | 1803.01449 | papers/SJzMATlAZ.pdf | f7c1c1ff97a746db6ca181809bf69b5c4c78d4f5abd5d6c2dc2d7494ac699021 | 991,706 | openreview | https://github.com/shahsohil/DCC | shahsohil/DCC | 775e07e17547df2804fd9eead3f1b128d303b577 | repos/SJzMATlAZ.zip | 74c957409776942d41486910683790491c7a3ec12fb53b4c59bdf5666bc04029 | 35,209 | 16 | {
".py": 16
} | 52 | {
"Python": 77928
} | false | 2021-07-14T09:52:55 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/deep-continuous-clustering"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
PZQHihJlfm | 2,026 | rejected | Next-Scale Autoregressive Models are Zero-Shot Single-Image Object View Synthesizers | [
"Shiran Yuan",
"Hao Zhao"
] | [
"~Shiran_Yuan1",
"~Hao_Zhao1"
] | OpenReview API | Learning to synthesize novel views without explicit 3D representations or hand-crafted 3D inductive bias has recently gained attention: it is simpler, more formally direct, and better aligned with the lesson that scalable learning paradigms with less assumptions built into architectural design (e.g., regarding geometry... | Reject | 4 | [
{
"id": "3pJsoMO7dZ",
"reviewer_signature": [
"ICLR.cc/2026/Conference/Submission3071/Reviewer_V295"
],
"rating": 6,
"soundness": 3,
"presentation": 2,
"contribution": 3,
"confidence": 4,
"summary": "The paper adopts a next-scale autoregressive model—specifically VAR—for si... | https://openreview.net/forum?id=PZQHihJlfm | 2503.13588 | papers/PZQHihJlfm.pdf | d0ae1551e84c4a5257f6aed353bf338866ade9f622998a2f9f330faee372a698 | 4,537,378 | openreview | https://github.com/Shiran-Yuan/ArchonView | Shiran-Yuan/ArchonView | 07be09646242b4625653c9d2ff1ec14330415655 | repos/PZQHihJlfm.zip | 3616eeed0dee05f406ada0d19b40b12ddc1ba5d2a28fecccb6948fffb737a2c7 | 47,624 | 17 | {
".py": 17
} | 74 | {
"Python": 141757
} | false | 2025-03-19T01:41:30 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/next-scale-autoregressive-models-are-zero"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} | |
3SMBSTG3qN | 2,025 | rejected | Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning | [
"Mehrdad Moghimi",
"Hyejin Ku"
] | [
"~Mehrdad_Moghimi1",
"~Hyejin_Ku1"
] | OpenReview API | In domains such as finance, healthcare, and robotics, managing worst-case scenarios is critical, as failure to do so can lead to catastrophic outcomes. Distributional Reinforcement Learning (DRL) provides a natural framework to incorporate risk sensitivity into decision-making processes. However, existing approaches fa... | Reject | 4 | [
{
"id": "3FC7IB4stR",
"reviewer_signature": [
"ICLR.cc/2025/Conference/Submission10312/Reviewer_tbS4"
],
"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 2,
"summary": "This work studies the problem of incorporating static spectral risk measu... | https://openreview.net/forum?id=3SMBSTG3qN | 2501.02087 | papers/3SMBSTG3qN.pdf | e907f006291434dc5a3999005872480a65130785f13be089928628de07bfde18 | 933,625 | openreview | https://github.com/MehrdadMoghimi/QRSRM | MehrdadMoghimi/QRSRM | ed267453bc326f0ff79f4a530480ffc6232906ba | repos/3SMBSTG3qN.zip | 0a5dba171dbb8a64b2aa347e50a68c341bae53204c01d8923370e72bffa9ad99 | 36,988 | 8 | {
".py": 8
} | 25 | {
"Python": 132192
} | false | 2025-12-30T04:17:41 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/beyond-cvar-leveraging-static-spectral-risk"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} | |
cSSHiLnjsJ | 2,024 | rejected | Traveling Words: A Geometric Interpretation of Transformers | [
"Raul Molina"
] | [
"~Raul_Molina1"
] | OpenReview API | Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer... | Reject | 4 | [
{
"id": "9gzbhgbZvZ",
"reviewer_signature": [
"ICLR.cc/2024/Conference/Submission986/Reviewer_rASQ"
],
"rating": "3: reject, not good enough",
"soundness": "1 poor",
"presentation": "1 poor",
"contribution": "2 fair",
"confidence": "4: You are confident in your assessment, but ... | https://openreview.net/forum?id=cSSHiLnjsJ | 2309.07315 | papers/cSSHiLnjsJ.pdf | 8b7b86a04e6113f4d813ae3107bb40f8e1dcd4980ab7416e88021927f4faae3a | 1,715,297 | openreview | https://github.com/santiag0m/traveling-words | santiag0m/traveling-words | 137aa6090aef2d3d954998bb3557f9e4a0b1ab41 | repos/cSSHiLnjsJ.zip | b73afe4e30e50ce5d2e2305b7d07a95b99b6710c3bf67e4354ea2bc611de4084 | 18,095 | 9 | {
".py": 9
} | 20 | {
"Python": 42512
} | false | 2023-12-20T22:13:49 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/traveling-words-a-geometric-interpretation-of"
} | {
"paper_reviews_decision": "OpenReview API",
"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
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