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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
EBBeSbmAyh | 2,025 | rejected | Towards Constraint-aware Learning for Resource Allocation in NFV-enabled Networks | [
"Tianfu Wang",
"Long Yang",
"Chao Wang",
"Chuan Qin",
"Liwei Deng",
"Li Shen",
"Hui Xiong"
] | [
"~Tianfu_Wang4",
"~Long_Yang4",
"~Chao_Wang14",
"~Chuan_Qin1",
"~Liwei_Deng2",
"~Li_Shen1",
"~Hui_Xiong1"
] | OpenReview API | Virtual Network Embedding (VNE) is a challenging combinatorial optimization problem that refers to resource allocation associated with hard and multifaceted constraints in network function virtualization (NFV). Existing works for VNE struggle to handle such complex constraints, leading to compromised system performance... | Reject | 4 | [
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"rating": 3,
"soundness": 2,
"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "The paper proposes a new framework called constraint-Aware Learning (CONAL... | https://openreview.net/forum?id=EBBeSbmAyh | 2410.22999 | papers/EBBeSbmAyh.pdf | 1fd5a143bda9078bf1b4442865b055aff1e04de08fe1c354dca8382bbb389656 | 1,182,935 | openreview | https://github.com/GeminiLight/conal-vne | GeminiLight/conal-vne | 768b322f314d4d33f9ceddeb2a27bf031ba5ee10 | repos/EBBeSbmAyh.zip | f3ecb0c7c22d5fa24e82db96834d40bc827cd4fb8dcb4ecd047766945181edaf | 435,026 | 98 | {
".py": 91,
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} | 390 | {
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} | false | 2024-10-11T07:01:53 | {
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xsts7MRLey | 2,024 | rejected | DEEP UNSUPERVISED DOMAIN ADAPTATION FOR TIME SERIES CLASSIFICATION: A BENCHMARK | [
"Hassan Ismail Fawaz",
"Ganesh Del Grosso",
"Tanguy Kerdoncuff",
"Aurelie Boisbunon",
"Illyyne Saffar"
] | [
"~Hassan_Ismail_Fawaz1",
"~Ganesh_Del_Grosso1",
"~Tanguy_Kerdoncuff1",
"~Aurelie_Boisbunon1",
"~Illyyne_Saffar1"
] | OpenReview API | Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data. Despite extensive research in domains like computer vision and natural language processing, UDA remains underexplored for time series data, which has widespread real-world applications ranging from medici... | Reject | 3 | [
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],
"rating": "5: marginally below the acceptance threshold",
"soundness": "2 fair",
"presentation": "2 fair",
"contribution": "2 fair",
"confidence": "3: You are fairly confiden... | https://openreview.net/forum?id=xsts7MRLey | 2312.09857 | papers/xsts7MRLey.pdf | a57654ccc36c0d8d4e3c007c2876ba573824fefac66092b0bc31a220e2cd76ba | 2,251,960 | openreview | https://github.com/EricssonResearch/UDA-4-TSC | EricssonResearch/UDA-4-TSC | c402cfc94673b8436e3dde4849342fded29e36a0 | repos/xsts7MRLey.zip | 509c9b272f11bd3773db2011aaec39b8e34185a86d7cca06819842b732a0ca78 | 349,475 | 90 | {
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".sh": 1
} | 374 | {
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} | false | 2023-12-19T09:08:18 | {
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mPxsHDgsimT | 2,023 | rejected | Subclass-balancing Contrastive Learning for Long-tailed Recognition | [
"Chengkai Hou",
"Jieyu Zhang",
"Haonan Wang",
"Tianyi Zhou"
] | [
"~Chengkai_Hou1",
"~Jieyu_Zhang1",
"~Haonan_Wang1",
"~Tianyi_Zhou1"
] | OpenReview API | Long-tailed recognition with imbalanced classes naturally emerges in practical machine learning applications. Existing methods such as data reweighing, resampling, and supervised contrastive learning enforce the class balance with a price of introducing imbalance between instances of head class and tail class, which ma... | 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=mPxsHDgsimT | 2306.15925 | papers/mPxsHDgsimT.pdf | 79b49f8a1698c018405b3ea53ba5e9fae7213cbfe0ba041645313f169ca61fc8 | 3,434,710 | openreview | https://github.com/JackHck/SBCL | JackHck/SBCL | 3b9047a31d5c54a0ac14cde351ab557d2833611e | repos/mPxsHDgsimT.zip | f3b0b31352b49e0e9982cf2de3aef5283807a44f57bdd756751d35eb37c58edc | 911,392 | 19 | {
".py": 19
} | 1,039 | {
"Python": 156976
} | false | 2023-10-30T09:23:27 | {
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} |
-uPIaaZdMLF | 2,022 | rejected | Attentional meta-learners for few-shot polythetic classification | [
"Ben Day",
"Ramon Viñas Torné",
"Nikola Simidjievski",
"Pietro Lio"
] | [
"~Ben_Day1",
"~Ramon_Viñas_Torné1",
"~Nikola_Simidjievski1",
"~Pietro_Lio1"
] | OpenReview API | Polythetic classifications, based on shared patterns of features that need neither be universal nor constant among members of a class, are common in the natural world and greatly outnumber monothetic classifications over a set of features. We show that threshold meta-learners, such as Prototypical Networks, require an ... | 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=-uPIaaZdMLF | 2106.05317 | papers/-uPIaaZdMLF.pdf | eba9d7222c7ac6cfe16aa36bc37152d58c64f1d5e7c3e555dafc4abc0c6a11dc | 3,574,651 | openreview | https://github.com/rvinas/polythetic_metalearning | rvinas/polythetic_metalearning | 97f510ae0810035e51be09c8542e6eed2df66d69 | repos/-uPIaaZdMLF.zip | ade1343564d7e653ab3e919e52d915d570a9801db018d4db21e4097aaa6f97d6 | 4,553,622 | 26 | {
".py": 18,
".ipynb": 8
} | 4,460 | {
"Jupyter Notebook": 4813353,
"Python": 110683
} | false | 2022-10-02T01:07:13 | {
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1AyPW2Emp6 | 2,021 | rejected | Tight Second-Order Certificates for Randomized Smoothing | [
"Alexander Levine",
"Aounon Kumar",
"Tom Goldstein",
"Soheil Feizi"
] | [
"~Alexander_Levine2",
"aounon@umd.edu",
"~Tom_Goldstein1",
"~Soheil_Feizi2"
] | OpenReview API | Randomized smoothing is a popular way of providing robustness guarantees against adversarial attacks: randomly-smoothed functions have a universal Lipschitz-like bound, allowing for robustness certificates to be easily computed. In this work, we show that there also exists a universal curvature-like bound for Gaussian ... | Reject | null | 3 | [
{
"id": "ayGHDsDvMIN",
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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",
... | https://openreview.net/forum?id=1AyPW2Emp6 | 2010.10549 | papers/1AyPW2Emp6.pdf | 2641e8b2bb4d104d85069fcd8502bfb0d6d79206103d40ca3e3777f6fc8192f6 | 5,942,360 | openreview | https://github.com/alevine0/smoothing_second_order | alevine0/smoothing_second_order | ae4b93c5666c3c967f28d62e770696fc2a417435 | repos/1AyPW2Emp6.zip | ecec5e74447b6fb7ef37cc7605032c64ae9353d88899bcf93dbaa810f31a0d97 | 2,818,537 | 18 | {
".py": 18
} | 2,703 | {
"Python": 85059
} | false | 2022-08-22T15:50:16 | {
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} |
8NtEHw8c8C | 2,026 | rejected | Probing Neural Topology of Large Language Models | [
"Yu Zheng",
"Yuan Yuan",
"Yue Zhuo",
"Yong Li",
"Paolo Santi"
] | [
"~Yu_Zheng7",
"~Yuan_Yuan15",
"~Yue_Zhuo2",
"~Yong_Li7",
"~Paolo_Santi2"
] | OpenReview API | Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural activations to interpretable semantics. However, the complex mechanisms that link neuron’s functional co-activation with the emergent model capabilities remains largely unknown, hindering a deeper underst... | Reject | 3 | [
{
"id": "c7K57MysHx",
"reviewer_signature": [
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],
"rating": 4,
"soundness": 2,
"presentation": 2,
"contribution": 3,
"confidence": 5,
"summary": "This study proposes a novel method called Graph Probing, which analyzes t... | https://openreview.net/forum?id=8NtEHw8c8C | 2506.01042 | papers/8NtEHw8c8C.pdf | b6ae534da36f3dc7c6198bfd532c43ef7274663bcb4826c9fa0fd1ec303e4d7e | 2,985,489 | openreview | https://github.com/DavyMorgan/llm-graph-probing | DavyMorgan/llm-graph-probing | 370e592d67aee6bb4bd3ccf38999476094788d4f | repos/8NtEHw8c8C.zip | 5157a7a4c381783e7f252f109bf20e653a5fc9578bddcf981d412ec33ec2d1b1 | 3,776,330 | 34 | {
".py": 34
} | 8,626 | {
"Python": 218668
} | false | 2026-02-27T02:34:09 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/probing-neural-topology-of-large-language"
} | {
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} | |
EXaKfdsw04 | 2,025 | rejected | StepProof: Step-by-step verification of natural language mathematical proofs | [
"Xiaolin Hu",
"Qinghua Zhou",
"Bogdan Grechuk",
"Ivan Y Tyukin",
"Oliver Sutton"
] | [
"~Xiaolin_Hu7",
"~Qinghua_Zhou1",
"~Bogdan_Grechuk1",
"~Ivan_Y_Tyukin1",
"~Oliver_Sutton1"
] | OpenReview API | Interactive theorem provers (ITPs) are powerful tools for the formal verification of mathematical proofs down to the axiom level. However, their lack of a natural language interface remains a significant limitation. Recent advancements in large language models (LLMs) have enhanced the understanding of natural language ... | Reject | 4 | [
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],
"rating": 3,
"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "The paper introduces StepProof, a method designed to improve the verificat... | https://openreview.net/forum?id=EXaKfdsw04 | 2506.10558 | papers/EXaKfdsw04.pdf | 48437bb844c05dc0a6d09bb9861d6c3497293dfa30306d562793cf4f8f9e7604 | 1,365,628 | openreview | https://github.com/r1nIGa/STEP-PROOF | r1nIGa/STEP-PROOF | e5da5c3e115713e0072a6f62acef0340d8ab8f02 | repos/EXaKfdsw04.zip | 6b730c88c5c7b0c480c8bb8156c4210d44ea4e716fff607993fa85eb05052e10 | 404,606 | 3 | {
".py": 3
} | 392 | {
"Python": 40394,
"Isabelle": 504
} | false | 2025-05-06T16:15:48 | {
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} | {
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} | |
tf6nR1B8Nt | 2,024 | rejected | No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths | [
"Charles Guille-Escuret",
"Hiroki Naganuma",
"Kilian FATRAS",
"Ioannis Mitliagkas"
] | [
"~Charles_Guille-Escuret1",
"~Hiroki_Naganuma1",
"~Kilian_FATRAS1",
"~Ioannis_Mitliagkas1"
] | OpenReview API | Understanding the optimization dynamics of neural networks is necessary for closing the gap between theory and practice. Stochastic first-order optimization algorithms are known to efficiently locate favorable minima in deep neural networks. This efficiency, however, contrasts with the non-convex and seemingly complex ... | Reject | 4 | [
{
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],
"rating": "8: accept, good paper",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "4: You are confident in your assessment, but not ... | https://openreview.net/forum?id=tf6nR1B8Nt | 2306.11922 | papers/tf6nR1B8Nt.pdf | 4cf7f53c580bcd280882a892c38b7dd790e4d313a57458a02fc0f00d19f0f24f | 713,796 | openreview | https://github.com/Hiroki11x/LossLandscapeGeometry | Hiroki11x/LossLandscapeGeometry | adb17b0b342305c7b19ec68c39de0c0bef78d10e | repos/tf6nR1B8Nt.zip | f4a7abad49e9c3fff28c2fd645ac6dfb29b019fba50e7d06bb1320dc473a92b4 | 384,564 | 36 | {
".py": 21,
".sh": 15
} | 384 | {
"Shell": 2236497,
"Python": 113312
} | false | 2024-06-10T20:08:40 | {
"method": "exact_normalized_title",
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S80I3NwbbpS | 2,023 | rejected | CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling | [
"Jun Zhang",
"Shuyang Jiang",
"Jiangtao Feng",
"Lin Zheng",
"Lingpeng Kong"
] | [
"~Jun_Zhang27",
"~Shuyang_Jiang2",
"~Jiangtao_Feng1",
"~Lin_Zheng1",
"~Lingpeng_Kong1"
] | OpenReview API | Transformer has achieved remarkable success in language, image, and speech processing. Recently, various efficient attention architectures have been proposed to improve transformer's efficiency while largely preserving its efficacy, especially in modeling long sequences. A widely-used benchmark to test these efficient ... | Reject | null | 4 | [
{
"id": "FXXSNzW03Q",
"reviewer_signature": [
"ICLR.cc/2023/Conference/Paper1790/Reviewer_auuz"
],
"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=S80I3NwbbpS | 2210.07661 | papers/S80I3NwbbpS.pdf | 7e8413358dbe7bc2d7220fcfc45794e973f83d67a155cf34359c8e128fe77ef3 | 29,193,277 | openreview | https://github.com/Shark-NLP/CAB | Shark-NLP/CAB | 0e496969545d5cb7c76b87de56d9d8b7600d0ba4 | repos/S80I3NwbbpS.zip | da98e7625841fb76bb0538b4e6a886ec505c219e38ef8c4dd4bbeaf7b0d7d13d | 1,025,078 | 33 | {
".py": 27,
".sh": 6
} | 1,046 | {
"Python": 204944,
"Shell": 1948
} | false | 2023-07-02T11:23:47 | {
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ljCoTzUsdS | 2,022 | rejected | Distinguishing rule- and exemplar-based generalization in learning systems | [
"Ishita Dasgupta",
"Erin Grant",
"Thomas L. Griffiths"
] | [
"~Ishita_Dasgupta1",
"~Erin_Grant1",
"~Thomas_L._Griffiths1"
] | OpenReview API | Despite the increasing scale of datasets in machine learning, generalization to unseen regions of the data distribution remains crucial. Such extrapolation is by definition underdetermined and is dictated by a learner’s inductive biases. Machine learning systems often do not share the same inductive biases as humans an... | Reject | null | 4 | [
{
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"rating": "",
"confidence": "1: You are unable to assess this paper and have alerted the ACs to seek an opinion from different reviewers.",
"recommendation": "5: marginally below the accep... | https://openreview.net/forum?id=ljCoTzUsdS | 2110.04328 | papers/ljCoTzUsdS.pdf | 9d5f5cbe3ec8678839d853e81fb3fe6e9971d6cc1148f0606440c19023af2bdb | 2,408,901 | openreview | https://github.com/eringrant/icml-2022-rules-vs-exemplars | eringrant/icml-2022-rules-vs-exemplars | c63fc90aa5862eeb929f66f29e9837fba96be8d4 | repos/ljCoTzUsdS.zip | 1fccbf918ec31e074311383e62fb136206c74fcc5cee25f2c92b80e44f025f5d | 4,767,240 | 20 | {
".py": 12,
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".r": 1
} | 4,648 | {
"Jupyter Notebook": 6727174,
"Python": 58534,
"R": 9956
} | false | 2023-11-15T15:48:12 | {
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IU8QxEiG4hR | 2,021 | rejected | SBEVNet: End-to-End Deep Stereo Layout Estimation | [
"Divam Gupta",
"Wei Pu",
"Trenton Tabor",
"Jeff Schneider"
] | [
"~Divam_Gupta1",
"wpu@nrec.ri.cmu.edu",
"~Trenton_Tabor1",
"~Jeff_Schneider1"
] | OpenReview API | Accurate layout estimation is crucial for planning and navigation, for robotics applications such as self driving. In this paper, we introduce stereo bird's eye view network SBEVNet, a novel supervised end-to-end framework for estimation of bird's eye view layout from a pair of stereo images. Although our network reuse... | Reject | null | 4 | [
{
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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",
... | https://openreview.net/forum?id=IU8QxEiG4hR | 2105.11705 | papers/IU8QxEiG4hR.pdf | 4253842fa38ef10ace1666b6c0e3d97a4f42036658048558156879b86191aa44 | 2,489,012 | openreview | https://github.com/divamgupta/sbevnet-stereo-layout-estimation | divamgupta/sbevnet-stereo-layout-estimation | ef9377df95041399bc3a243fefdbb343effca12c | repos/IU8QxEiG4hR.zip | 647abfef2e29f3563a69a49f81dda893dd8c1987de7b366f1b9f3072333a33d4 | 2,849,843 | 10 | {
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} | 2,783 | {
"Python": 45584
} | false | 2023-01-15T17:05:49 | {
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Oo5t7b1jQu | 2,026 | rejected | SituatedThinker: Grounding LLM Reasoning with Real-World through Situated Thinking | [
"Junnan Liu",
"Linhao Luo",
"Thuy-Trang Vu",
"Gholamreza Haffari"
] | [
"~Junnan_Liu1",
"~Linhao_Luo2",
"~Thuy-Trang_Vu1",
"~Gholamreza_Haffari2"
] | OpenReview API | Recent advances in large language models (LLMs) demonstrate their impressive reasoning capabilities. However, the reasoning confined to internal parametric space limits LLMs' access to real-time information and understanding of the physical world. To overcome this constraint, we introduce SituatedThinker, a novel frame... | Reject | 4 | [
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"rating": 4,
"soundness": 3,
"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "This paper proposes a framework, SituatedThinker, which aims to ground th... | https://openreview.net/forum?id=Oo5t7b1jQu | 2505.19300 | papers/Oo5t7b1jQu.pdf | c063c7b1bca364a3725ed5cc1c94c8c8a3f478686346260922b5c4a1ea0dbb09 | 765,445 | openreview | https://github.com/jnanliu/SituatedThinker | jnanliu/SituatedThinker | 6e44597f4a9e8725206106e2aebfb0cefe31c596 | repos/Oo5t7b1jQu.zip | 1eca409b74dd4f9c216f72ba7715c63ab749921c48ce1fa4279adc19d143f3b6 | 10,721,296 | 221 | {
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} | 9,318 | {
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} | false | 2025-06-08T18:17:29 | {
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} | {
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} | |
4RRmy9iw3c | 2,025 | rejected | AutoAL: Automated Active Learning with Differentiable Query Strategy Search | [
"Yifeng Wang",
"Xueying Zhan",
"Siyu Huang"
] | [
"~Yifeng_Wang2",
"~Xueying_Zhan1",
"~Siyu_Huang2"
] | OpenReview API | As deep learning continues to evolve, the need for data efficiency becomes increasingly important. Considering labeling large datasets is both time-consuming and expensive, active learning (AL) provides a promising solution to this challenge by iteratively selecting the most informative subsets of examples to train dee... | Reject | 4 | [
{
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"ICLR.cc/2025/Conference/Submission5471/Reviewer_fWbJ"
],
"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 5,
"summary": "This work introduces AutoAL, a differentiable active learning (AL) strateg... | https://openreview.net/forum?id=4RRmy9iw3c | 2410.13853 | papers/4RRmy9iw3c.pdf | 5e3b9a1dff3f9910694a4ea5f8407ab6a5102e18105bd949830937cdc32684b6 | 540,568 | openreview | https://github.com/haizailache999/AutoAL | haizailache999/AutoAL | 5200ce7b2780467e292001c3583b59e50fcfe6bc | repos/4RRmy9iw3c.zip | 80f54138388674310a5525fd94bba6e782d044bb727c78eb1ce0f29ec3ac6dd9 | 386,662 | 50 | {
".py": 50
} | 396 | {
"Python": 189805
} | false | 2025-05-18T23:50:02 | {
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"pwc_url": "https://paperswithcode.com/paper/autoal-automated-active-learning-with"
} | {
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"repository_snapshot": "GitHub API commit-pinned ZIP"
} | |
ICSvW69W5K | 2,024 | rejected | Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering | [
"Daehwan Nam",
"Gary Lee"
] | [
"~Daehwan_Nam1",
"~Gary_Lee1"
] | OpenReview API | Semantic parsers convert natural language to logical forms, which can then be evaluated on knowledge bases (KBs) to produce denotations.
Early neural semantic parsers used grammars that define actions, such as production rules, then the semantic parsers could sequentially take actions to construct well-typed logical fo... | Reject | 4 | [
{
"id": "SHu5dbPRoR",
"reviewer_signature": [
"ICLR.cc/2024/Conference/Submission2430/Reviewer_Ntkc"
],
"rating": "5: marginally below the acceptance threshold",
"soundness": "4 excellent",
"presentation": "4 excellent",
"contribution": "2 fair",
"confidence": "4: You are confi... | https://openreview.net/forum?id=ICSvW69W5K | 2410.00414 | papers/ICSvW69W5K.pdf | e9f74dd2245bdb9384e5caa87a0c8712cb258033bf112f5a99f9c055584ec706 | 413,947 | openreview | https://github.com/daehwannam/candexpr-sp | daehwannam/candexpr-sp | 0e6a168e3565c40672970ad28ee7caa88d0a7936 | repos/ICSvW69W5K.zip | 6ca9ff2b9aaadd46ec9e094a13da7f10c1e28ce437eee5d3c7d99503c6616c6f | 166,866 | 142 | {
".py": 139,
".sh": 3
} | 412 | {
"Python": 402632,
"Emacs Lisp": 26132,
"Shell": 1263,
"TeX": 557
} | false | 2025-12-31T04:42:15 | {
"method": "exact_normalized_title",
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"pwc_url": "https://paperswithcode.com/paper/semantic-parsing-with-candidate-expressions"
} | {
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} | |
dF0g-5k05h_ | 2,023 | rejected | The Vendi Score: A Diversity Evaluation Metric for Machine Learning | [
"Dan Friedman",
"Adji Bousso Dieng"
] | [
"~Dan_Friedman2",
"~Adji_Bousso_Dieng1"
] | OpenReview API | Diversity is an important criterion for many areas of machine learning (ML), including generative modeling and dataset curation. Yet little work has gone into understanding, formalizing, and measuring diversity in ML. In this paper we address the diversity evaluation problem by proposing the Vendi Score, which connects... | Reject | null | 3 | [
{
"id": "71uc0RDj8g6",
"reviewer_signature": [
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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=dF0g-5k05h_ | 2210.02410 | papers/dF0g-5k05h_.pdf | fadffb85538c503eea30faad855d8fd857e7dc8a77a1eef2c5b08f8029b4a9b5 | 8,534,544 | openreview | https://github.com/vertaix/Vendi-Score | vertaix/Vendi-Score | ff1dfdbe6356b98a6087540f215b9a9db6db7c11 | repos/dF0g-5k05h_.zip | 65289de42582014b90d3e9d265cc4f2ed4bddee1c9e75f2893d2e406e6ee5650 | 1,132,711 | 9 | {
".py": 6,
".ipynb": 3
} | 1,139 | {
"Python": 17064
} | false | 2025-07-27T11:18:24 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/the-vendi-score-a-diversity-evaluation-metric"
} | {
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"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
m22XrToDacC | 2,022 | rejected | Distributionally Robust Recourse Action | [
"Duy Nguyen",
"Ngoc Bui",
"Viet Anh Nguyen"
] | [
"~Duy_Nguyen2",
"~Ngoc_Bui1",
"~Viet_Anh_Nguyen2"
] | OpenReview API | Recourse actions, also known as counterfactual explanations, aim to explain a particular algorithmic decision by showing one or multiple ways in which the instance could be modified to receive an alternate outcome. Existing recourse recommendations often assume that the machine learning models do not change over time. ... | 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 piece... | https://openreview.net/forum?id=m22XrToDacC | 2302.11211 | papers/m22XrToDacC.pdf | 3b727f15e569b77d6befdee9902200ead6f72e5c4b2785f2cd8c28f506197afe | 619,372 | openreview | https://github.com/duykhuongnguyen/DiRRAc | duykhuongnguyen/DiRRAc | 928e021fed815b63b351cb79964530c6bd3d35b8 | repos/m22XrToDacC.zip | ae30c2fc7c948680f0a20460bb8555ad2b713e2721a9589973ff3c48638d86da | 5,183,492 | 56 | {
".py": 48,
".ipynb": 8
} | 4,779 | {
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"Shell": 2772
} | false | 2023-02-13T03:03:40 | {
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} | {
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} |
pwwVuSICBgt | 2,021 | rejected | Enabling Binary Neural Network Training on the Edge | [
"Erwei Wang",
"James J. Davis",
"Daniele Moro",
"Piotr Zielinski",
"Claudionor Coelho",
"Satrajit Chatterjee",
"Peter Y. K. Cheung",
"George Anthony Constantinides"
] | [
"~Erwei_Wang1",
"james.davis@imperial.ac.uk",
"danielemoro@google.com",
"~Piotr_Zielinski1",
"claudionor.coelho@alumni.stanford.edu",
"~Satrajit_Chatterjee1",
"p.cheung@imperial.ac.uk",
"~George_Anthony_Constantinides1"
] | OpenReview API | The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Binary neural networks are known to be promising candidates for on-device inference due to their extreme compute and memory savings over higher... | Reject | null | 4 | [
{
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],
"rating": "5: Marginally below acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"... | https://openreview.net/forum?id=pwwVuSICBgt | 2102.04270 | papers/pwwVuSICBgt.pdf | f4e1e877ae21f7ad9963c980bd59656a790b0979441226ac6a7f7c536784be73 | 457,318 | openreview | https://github.com/awai54st/Enabling-Binary-Neural-Network-Training-on-the-Edge | awai54st/Enabling-Binary-Neural-Network-Training-on-the-Edge | b2c026b09e81ea01c778a4d500af99df612a7a63 | repos/pwwVuSICBgt.zip | 1e9caef5bc332ac902f9b23fa3ff6240afff13b531d0b7090b86363f1805b295 | 3,917,263 | 190 | {
".cpp": 93,
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} | 2,901 | {
"C++": 548945,
"Python": 291996,
"Jupyter Notebook": 283149,
"C": 1099
} | false | 2022-03-06T22:03:47 | {
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} | {
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} |
rVrWNb2XLi | 2,026 | rejected | SpecOffload: Unlocking Latent GPU Capacity for LLM Inference on Resource-Constrained Devices | [
"Xiangwen Zhuge",
"Xu Shen",
"Zeyu Wang",
"Fan Dang",
"Xuan Ding",
"Danyang Li",
"Yahui Han",
"Tianxiang Hao",
"Zheng Yang"
] | [
"~Xiangwen_Zhuge1",
"~Xu_Shen5",
"~Zeyu_Wang28",
"~Fan_Dang1",
"~Xuan_Ding3",
"~Danyang_Li3",
"~Yahui_Han1",
"~Tianxiang_Hao1",
"~Zheng_Yang1"
] | OpenReview API | Efficient LLM inference on resource-constrained devices (i.e., PCs with a single commodity GPU) presents significant challenges in compute and memory utilization. Due to limited GPU memory, existing systems offload model weights to CPU memory, incurring substantial I/O overhead between the CPU and GPU. This leads to tw... | Reject | 4 | [
{
"id": "iEaO7KSjcn",
"reviewer_signature": [
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],
"rating": 6,
"soundness": 3,
"presentation": 2,
"contribution": 3,
"confidence": 3,
"summary": "This paper proposes an LLM inference system that combines offloading and ... | https://openreview.net/forum?id=rVrWNb2XLi | 2505.10259 | papers/rVrWNb2XLi.pdf | 6f7983de61f4ab2a0458a44f9bfd9e608ef149560697e72ae2e5746c6facd019 | 775,442 | openreview | https://github.com/MobiSense/SpecOffload-public | MobiSense/SpecOffload-public | b8b68c9f4278fd9ef9b585b3e48ede301747a212 | repos/rVrWNb2XLi.zip | 7392869efc35f918254a472b51466eed964b47c215476934254c16c8aa0e8d4a | 11,599,378 | 1,872 | {
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".cu": 8,
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".cuh": 4
} | 9,693 | {
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"Cuda": 327808,
"C++": 25815,
"Shell": 18501,
"C": 7703,
"Cython": 3635
} | false | 2026-02-03T02:04:10 | {
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"pwc_url": "https://paperswithcode.com/paper/specoffload-unlocking-latent-gpu-capacity-for"
} | {
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} | |
ln2k0PqVQA | 2,025 | rejected | Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback | [
"Qinqing Zheng",
"Mikael Henaff",
"Amy Zhang",
"Aditya Grover",
"Brandon Amos"
] | [
"~Qinqing_Zheng1",
"~Mikael_Henaff1",
"~Amy_Zhang1",
"~Aditya_Grover1",
"~Brandon_Amos1"
] | OpenReview API | Automatically synthesizing dense rewards from natural language descriptions is a promising paradigm in reinforcement learning (RL), with applications to sparse reward problems, open-ended exploration, and hierarchical skill design. Recent works have made promising steps by exploiting the prior knowledge of large langua... | Reject | 4 | [
{
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],
"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This work presents a distributed system and an online learning algorithm f... | https://openreview.net/forum?id=ln2k0PqVQA | 2410.23022 | papers/ln2k0PqVQA.pdf | 71573254cebaf2c16434171e4fa902a3a50e4cb51a85bd32f1aa3d6969b08229 | 19,114,039 | openreview | https://github.com/facebookresearch/oni | facebookresearch/oni | f0f86e8abea877e045e74f4bf786d246df0638a4 | repos/ln2k0PqVQA.zip | 526b5ec20098373b53045c5d381c356896654e9b0c09f708a00b8192ee5adf74 | 463,653 | 135 | {
".py": 132,
".sh": 3
} | 400 | {
"Python": 783791,
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} | true | 2024-12-17T22:07:27 | {
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"pwc_url": "https://paperswithcode.com/paper/online-intrinsic-rewards-for-decision-making"
} | {
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} | |
xbXASfz8MD | 2,024 | rejected | Latent Space Symmetry Discovery | [
"Jianke Yang",
"Nima Dehmamy",
"Robin Walters",
"Rose Yu"
] | [
"~Jianke_Yang2",
"~Nima_Dehmamy1",
"~Robin_Walters1",
"~Rose_Yu1"
] | OpenReview API | Equivariant neural networks require explicit knowledge of the symmetry group. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivariance from data. However, existing symmetry discovery methods are limited to linear symmetries in their search space and cannot handle the compl... | Reject | 3 | [
{
"id": "KnakcOYDMD",
"reviewer_signature": [
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],
"rating": "8: accept, good paper",
"soundness": "4 excellent",
"presentation": "4 excellent",
"contribution": "4 excellent",
"confidence": "5: You are absolutely certain about... | https://openreview.net/forum?id=xbXASfz8MD | 2310.00105 | papers/xbXASfz8MD.pdf | bc47a63f3f0c138b41b433b22193b53cb15113b36d3d64755775fa1bccc7f09a | 4,034,124 | openreview | https://github.com/jiankeyang/LaLiGAN | jiankeyang/LaLiGAN | 030457ae5b23555f175bf8422f8d3fb99942973e | repos/xbXASfz8MD.zip | 035ee4299e271ef64edae1bc517d9180379fbd565c4983c6ab33371892534599 | 385,964 | 14 | {
".py": 14
} | 423 | {
"Python": 83625
} | false | 2024-07-12T23:25:40 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/latent-space-symmetry-discovery"
} | {
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} | |
YP4QEmqh6Ia | 2,023 | rejected | Which Invariance Should We Transfer? A Causal Minimax Learning Approach | [
"Mingzhou Liu",
"Xiangyu Zheng",
"Xinwei Sun",
"Fang Fang",
"Yizhou Wang"
] | [
"~Mingzhou_Liu1",
"~Xiangyu_Zheng1",
"~Xinwei_Sun1",
"~Fang_Fang1",
"~Yizhou_Wang1"
] | OpenReview API | A major barrier to deploy current machine learning models lies in their sensitivity to dataset shifts. To resolve this problem, most existing studies attempted to transfer stable information to unseen environments. Among these, graph-based methods causally decomposed the data generating process into stable and mutable ... | 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=YP4QEmqh6Ia | 2107.01876 | papers/YP4QEmqh6Ia.pdf | d60bf2b3bc7b8ae1901261511537a029607437d18640717e635cb74956258376 | 1,611,158 | openreview | https://github.com/lmz123321/which_invariance | lmz123321/which_invariance | 707f2772ad121011f49ce8bcc7b04da65b13f911 | repos/YP4QEmqh6Ia.zip | 41f602cc266e04fb58e39800bee3948905cd3d1e1c637a4aaf5e295fcdc77281 | 2,811,752 | 31 | {
".py": 25,
".ipynb": 5,
".r": 1
} | 1,179 | {
"Jupyter Notebook": 163253,
"Python": 158335,
"R": 15179
} | false | 2024-05-27T03:18:07 | {
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"pwc_url": "https://paperswithcode.com/paper/causally-invariant-predictor-with-shift"
} | {
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FYUzzBPh_j | 2,022 | rejected | Communicating via Markov Decision Processes | [
"Samuel Sokota",
"Christian Schroeder de Witt",
"Maximilian Igl",
"Luisa M Zintgraf",
"Philip Torr",
"J Zico Kolter",
"Shimon Whiteson",
"Jakob Nicolaus Foerster"
] | [
"~Samuel_Sokota1",
"~Christian_Schroeder_de_Witt1",
"~Maximilian_Igl1",
"~Luisa_M_Zintgraf1",
"~Philip_Torr1",
"~J_Zico_Kolter1",
"~Shimon_Whiteson1",
"~Jakob_Nicolaus_Foerster1"
] | OpenReview API | We consider the problem of communicating exogenous information by means of Markov decision process trajectories. This setting, which we call a Markov coding game (MCG), generalizes both source coding and a large class of referential games. MCGs also isolate a problem that is important in decentralized control settings ... | Reject | null | 4 | [
{
"id": "er9o7CteU0i",
"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=FYUzzBPh_j | 2107.08295 | papers/FYUzzBPh_j.pdf | 7749fda12891515acd2506b592a93dd19cf0b3639f78540d6fadd42b0273bec5 | 531,562 | openreview | https://github.com/schroederdewitt/meme | schroederdewitt/meme | d94690ea7f4026a93d5e116d9ba643504f8f71fe | repos/FYUzzBPh_j.zip | ca98203639850ec49a54b357ccb59029b70caa168241b9b0068a866735d3b684 | 5,799,169 | 132 | {
".py": 121,
".sh": 10,
".cpp": 1
} | 4,936 | {
"Python": 1226433,
"C++": 13482,
"Shell": 3704,
"Makefile": 1976,
"Dockerfile": 1566
} | false | 2023-05-08T13:56:32 | {
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"pwc_url": "https://paperswithcode.com/paper/implicit-communication-as-minimum-entropy"
} | {
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hecuSLbL_vC | 2,021 | rejected | Generalisation Guarantees For Continual Learning With Orthogonal Gradient Descent | [
"Mehdi Abbana Bennani",
"Thang Doan",
"Masashi Sugiyama"
] | [
"~Mehdi_Abbana_Bennani1",
"~Thang_Doan1",
"~Masashi_Sugiyama1"
] | OpenReview API | In Continual Learning settings, deep neural networks are prone to Catastrophic Forgetting. Orthogonal Gradient Descent (Farajtabar et al., 2019) was proposed to tackle the challenge. However, no theoretical guarantees have been proven yet. We present a theoretical framework to study Continual Learning algorithms in the... | Reject | null | 3 | [
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"id": "x0qkc0FQgL0",
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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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ycxzArIvgF | 2,026 | rejected | Effective Data Pruning through Score Extrapolation | [
"Sebastian Schmidt",
"Prasanga Dhungel",
"Christoffer Löffler",
"Björn Nieth",
"Stephan Günnemann",
"Leo Schwinn"
] | [
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"~Prasanga_Dhungel1",
"~Christoffer_Löffler1",
"~Björn_Nieth1",
"~Stephan_Günnemann1",
"~Leo_Schwinn1"
] | OpenReview API | Training advanced machine learning models demands massive datasets, resulting in prohibitive computational costs. To address this challenge, data pruning techniques identify and remove redundant training samples while preserving model performance. Yet, existing pruning techniques predominantly require a full initial tr... | Reject | 4 | [
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"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "This paper addresses the computational efficiency problem in data pruning... | https://openreview.net/forum?id=ycxzArIvgF | 2506.09010 | papers/ycxzArIvgF.pdf | 31d3ed279e251af304d61c1ef614fac09379f1030a640da367a915bbd751b19d | 4,263,230 | openreview | https://github.com/prasangadhungel/Data-Pruning-with-Extrapolated-Scores | prasangadhungel/Data-Pruning-with-Extrapolated-Scores | 655e48fbb48e5856da9052808222388cfc6d99f2 | repos/ycxzArIvgF.zip | 228a0a556f1c6d16a13b65163b1b9b76ed08d5fa42ec4bd2a2a58ee2e18ebb78 | 112,358 | 34 | {
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LC2KxRwC3n | 2,025 | rejected | A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders | [
"David Chanin",
"James Wilken-Smith",
"Tomáš Dulka",
"Hardik Bhatnagar",
"Joseph Isaac Bloom"
] | [
"~David_Chanin1",
"~James_Wilken-Smith1",
"~Tomáš_Dulka1",
"~Hardik_Bhatnagar1",
"~Joseph_Isaac_Bloom1"
] | OpenReview API | Sparse Autoencoders (SAEs) have emerged as a promising approach to decompose the activations of Large Language Models (LLMs) into human-interpretable latents. In this paper, we pose two questions. First, to what extent do SAEs extract monosemantic and interpretable latents? Second, to what extent does varying the spars... | Reject | 4 | [
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"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 3,
"summary": "This paper explores the use of Sparse Autoencoders (SAEs) to decompose act... | https://openreview.net/forum?id=LC2KxRwC3n | 2409.14507 | papers/LC2KxRwC3n.pdf | 97ffad0178188e8cc48a0bb2614ca50956afcfd9623bb44026f37ff13f16a238 | 3,838,697 | openreview | https://github.com/lasr-spelling/sae-spelling | lasr-spelling/sae-spelling | 515648a5b96dc389ba317c4053fd8aa6eaca9929 | repos/LC2KxRwC3n.zip | 85ed4b95247f9f8ef69fee70664f70d3b44ef86c802c4d3f4565bac05a7adb2c | 234,672 | 32 | {
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} | 400 | {
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I5lcjmFmlc | 2,024 | rejected | Robust Classification via a Single Diffusion Model | [
"Huanran Chen",
"Yinpeng Dong",
"Zhengyi Wang",
"Xiao Yang",
"Chengqi Duan",
"Hang Su",
"Jun Zhu"
] | [
"~Huanran_Chen1",
"~Yinpeng_Dong2",
"~Zhengyi_Wang1",
"~Xiao_Yang4",
"~Chengqi_Duan1",
"~Hang_Su3",
"~Jun_Zhu2"
] | OpenReview API | Recently, diffusion models have been successfully applied to improving adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training. However, the diffusion-based purification can be evaded by stronger adaptive attacks while adversarial training do... | Reject | 3 | [
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"rating": "8: accept, good paper",
"soundness": "3 good",
"presentation": "2 fair",
"contribution": "4 excellent",
"confidence": "4: You are confident in your assessment, but n... | https://openreview.net/forum?id=I5lcjmFmlc | 2305.15241 | papers/I5lcjmFmlc.pdf | 8609580e25373aaec4239effe0d3e7ca16318c1f5f77c311aa9ba6d21bcdab2c | 2,716,088 | openreview | https://github.com/huanranchen/DiffusionClassifier | huanranchen/DiffusionClassifier | 8a825045d91ab50c98503933c874a4296c5b2bec | repos/I5lcjmFmlc.zip | ff237233e91228f5d66396b1d92ff3fece9b3b6a963447c16057a6b505a29054 | 549,418 | 282 | {
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} | 437 | {
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} | false | 2025-03-07T09:08:25 | {
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C9sU3Tnnki8 | 2,023 | rejected | Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation | [
"YiFan Zhang",
"Hanlin Zhang",
"Zachary Chase Lipton",
"Li Erran Li",
"Eric Xing"
] | [
"~YiFan_Zhang8",
"~Hanlin_Zhang1",
"~Zachary_Chase_Lipton1",
"~Li_Erran_Li1",
"~Eric_Xing1"
] | OpenReview API | Previous works on Treatment Effect Estimation (TEE) are not in widespread use because they are predominantly theoretical, where strong parametric assumptions are made but untractable for practical application. Recent works use Multilayer Perceptron (MLP) for modeling casual relationships, however, MLPs lag far behind r... | Reject | null | 3 | [
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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=C9sU3Tnnki8 | 2202.01336 | papers/C9sU3Tnnki8.pdf | d79793737fd52fbf61c0c613fd81558970a912d4679d85fc6055ecc156fcb819 | 1,659,403 | openreview | https://github.com/hlzhang109/TransTEE | hlzhang109/TransTEE | 3e6883a7edd7d780185d524553b13ae633b6a229 | repos/C9sU3Tnnki8.zip | 99650922ea0a0c2cdfb009bc72b9bb866c0a7aef2052a42962b158fc1ff77c50 | 1,274,747 | 123 | {
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} | false | 2025-09-13T04:32:03 | {
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qEGBB9YB31 | 2,022 | rejected | Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability | [
"Roman Levin",
"Manli Shu",
"Eitan Borgnia",
"Furong Huang",
"Micah Goldblum",
"Tom Goldstein"
] | [
"~Roman_Levin1",
"~Manli_Shu1",
"~Eitan_Borgnia1",
"~Furong_Huang1",
"~Micah_Goldblum1",
"~Tom_Goldstein1"
] | OpenReview API | Conventional saliency maps highlight input features to which neural network predictions are highly sensitive. We take a different approach to saliency, in which we identify and analyze the network parameters, rather than inputs, which are responsible for erroneous decisions. We first verify that identified salient par... | 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=qEGBB9YB31 | 2108.01335 | papers/qEGBB9YB31.pdf | b68ead11c290531cc1a8fe901907a7727fab46ac5d5941c90224129bcbf3efa9 | 13,377,231 | openreview | https://github.com/LevinRoman/parameter-space-saliency | LevinRoman/parameter-space-saliency | 0e3b3d69c6e222aee6af0264d7ce3ddc6d19744e | repos/qEGBB9YB31.zip | 1c97e98c4908db622223bf0705626adcfe9a2d6d060d70e3e47d5814a7ac8938 | 5,409,635 | 3 | {
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} | 5,219 | {
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} | false | 2023-03-21T23:02:02 | {
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tf8a4jDRFCv | 2,021 | rejected | Learning Aggregation Functions | [
"Giovanni Pellegrini",
"Alessandro Tibo",
"Paolo Frasconi",
"Andrea Passerini",
"Manfred Jaeger"
] | [
"~Giovanni_Pellegrini1",
"~Alessandro_Tibo1",
"~Paolo_Frasconi1",
"~Andrea_Passerini2",
"jaeger@cs.aau.dk"
] | OpenReview API | Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as sum or maximum. However, recent results showed that universal function representation by sum- (or max... | Reject | null | 4 | [
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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",
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"s... | https://openreview.net/forum?id=tf8a4jDRFCv | 2012.08482 | papers/tf8a4jDRFCv.pdf | 21ae87a965314846c24a5d6a76ea2e46fb7044864a64c16bece07908fc5dfd7e | 2,388,924 | openreview | https://github.com/alessandro-t/laf | alessandro-t/laf | 670aafbad87292343de04fa7a16211fded484ce0 | repos/tf8a4jDRFCv.zip | 38e63ceb85e6265530147eed80f5d1f9ab58d94ca2c84c764177ac12314c466d | 2,981,004 | 10 | {
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} | 2,952 | {
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} | false | 2021-05-17T15:03:00 | {
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RZIfy4Qzxa | 2,026 | rejected | MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems | [
"Kai Chen",
"Taihang Zhen",
"Hewei Wang",
"Kailai Liu",
"Xinfeng Li",
"Jing Huo",
"Tianpei Yang",
"Jinfeng Xu",
"Wei Dong",
"Yang Gao"
] | [
"~Kai_Chen37",
"~Taihang_Zhen1",
"~Hewei_Wang1",
"~Kailai_Liu1",
"~Xinfeng_Li1",
"~Jing_Huo2",
"~Tianpei_Yang1",
"~Jinfeng_Xu2",
"~Wei_Dong5",
"~Yang_Gao3"
] | OpenReview API | As large language models are increasingly adopted in healthcare, ensuring their safety is critical, particularly in collaborative multi-agent settings. This paper develops an end-to-end attack–defense evaluation workflow to systematically analyze how four representative multi-agent topologies (Layers, SharedPool, Centr... | Reject | 4 | [
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"contribution": 3,
"confidence": 3,
"summary": "This paper introduces MedSentry, a benchmark with 5,000 adversarial medic... | https://openreview.net/forum?id=RZIfy4Qzxa | 2505.20824 | papers/RZIfy4Qzxa.pdf | 193101017c3a2ba53a647aca86c9d14ec99a7c308215be9119c26af27b32f27a | 3,930,456 | openreview | https://github.com/KaiChenNJ/MedSentry | KaiChenNJ/MedSentry | 044ba8b36e1bb30a78098b9e9a075072094081c4 | repos/RZIfy4Qzxa.zip | 7d5d6e75013ac52a428326c48d013c25acc814823bc7de50b92aecaf617d6a9e | 8,939,884 | 16 | {
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EWNH3QTSxd | 2,025 | rejected | Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences | [
"Takuya Hiraoka",
"Guanquan Wang",
"Takashi Onishi",
"Yoshimasa Tsuruoka"
] | [
"~Takuya_Hiraoka1",
"~Guanquan_Wang1",
"~Takashi_Onishi1",
"~Yoshimasa_Tsuruoka1"
] | OpenReview API | In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance.
Information about how these experiences influence the agent's performance is valuable for various purposes, such as identifying experiences that negatively influence underperforming agents... | Reject | 4 | [
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"presentation": 2,
"contribution": 1,
"confidence": 3,
"summary": "This paper aims to study the influence individual experience sample have in... | https://openreview.net/forum?id=EWNH3QTSxd | 2405.14629 | papers/EWNH3QTSxd.pdf | 0793ef11fd5ef27c26c488eed081c286bba735152ac3a53263d215c431a8be7e | 6,434,349 | openreview | https://github.com/TakuyaHiraoka/Which-Experiences-Are-Influential-for-RL-Agents | TakuyaHiraoka/Which-Experiences-Are-Influential-for-RL-Agents | 085d8fd2791a50e1aafc2309c35ac05c7863b7be | repos/EWNH3QTSxd.zip | 91afbba8af4b0ba18a5f3a6af3e72f0775b7137cb8b683c8add7c70863f91d7a | 126,149 | 18 | {
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EPfGHb9Y68 | 2,024 | rejected | Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay | [
"Jinmei Liu",
"Wenbin Li",
"Xiangyu Yue",
"Chunlin Chen",
"Zhi Wang"
] | [
"~Jinmei_Liu2",
"~Wenbin_Li5",
"~Xiangyu_Yue1",
"~Chunlin_Chen1",
"~Zhi_Wang7"
] | OpenReview API | We study continual offline reinforcement learning, a practical paradigm that facilitates forward transfer and mitigates catastrophic forgetting to tackle sequential offline tasks. We propose a dual generative replay framework that retains previous knowledge by concurrent replay of generated pseudo-data. First, we decou... | Reject | 4 | [
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"soundness": "2 fair",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "2: You are willing to defe... | https://openreview.net/forum?id=EPfGHb9Y68 | 2404.10662 | papers/EPfGHb9Y68.pdf | e223ceead279b0da07cba3ab31719f26777c65ce83a1fcbb4fe9fcac5665b330 | 1,330,490 | openreview | https://github.com/NJU-RL/CuGRO | NJU-RL/CuGRO | f3f09a4ac96e4e52a92cea946c98813480740af1 | repos/EPfGHb9Y68.zip | 92addfb40285eabf2d908b9c68798989216a044c430c2a8098964577fab19294 | 516,457 | 30 | {
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UhEJz3wgLnG | 2,023 | rejected | Revealing Single Frame Bias for Video-and-Language Learning | [
"Jie Lei",
"Tamara L Berg",
"Mohit Bansal"
] | [
"~Jie_Lei3",
"~Tamara_L_Berg1",
"~Mohit_Bansal2"
] | OpenReview API | Training an effective video-and-language model intuitively requires multiple frames as model inputs.
However, it is unclear whether using multiple frames is beneficial to downstream tasks, and if yes, whether the performance gain is worth the drastically-increased computation and memory costs resulting from using more... | Reject | null | 4 | [
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B0JH7vR2iGh | 2,022 | rejected | PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration | [
"Pengyi Li",
"Hongyao Tang",
"Tianpei Yang",
"Xiaotian Hao",
"Sang Tong",
"YAN ZHENG",
"Jianye HAO",
"Matthew E. Taylor",
"Jinyi Liu"
] | [
"~Pengyi_Li1",
"~Hongyao_Tang1",
"~Tianpei_Yang1",
"~Xiaotian_Hao1",
"~Sang_Tong1",
"~YAN_ZHENG1",
"~Jianye_HAO1",
"~Matthew_E._Taylor2",
"~Jinyi_Liu1"
] | OpenReview API | Learning to collaborate is critical in multi-agent reinforcement learning (MARL). A branch of previous works proposes to promote collaboration by maximizing the correlation of agents’ behaviors, which is typically characterised by mutual information (MI) in different forms. However, simply maximizing the MI of agents’ ... | Reject | null | 4 | [
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bVzUDC_4ls | 2,021 | rejected | Exploiting Verified Neural Networks via Floating Point Numerical Error | [
"Kai Jia",
"Martin Rinard"
] | [
"~Kai_Jia2",
"~Martin_Rinard1"
] | OpenReview API | Motivated by the need to reliably characterize the robustness of deep neural networks, researchers have developed verification algorithms for deep neural networks. Given a neural network, the verifiers aim to answer whether certain properties are guaranteed with respect to all inputs in a space. However, little attenti... | Reject | null | 4 | [
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} | false | 2021-10-16T15:33:03 | {
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mDEYl0Ucgr | 2,025 | rejected | Influencing Humans to Conform to Preference Models for RLHF | [
"Stephane Hatgis-Kessell",
"W. Bradley Knox",
"Serena Booth",
"Scott Niekum",
"Peter Stone"
] | [
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"~W._Bradley_Knox2",
"~Serena_Booth1",
"~Scott_Niekum1",
"~Peter_Stone1"
] | OpenReview API | Designing a reinforcement learning from human feedback (RLHF) algorithm for learning from preferences requires assuming a preference model, sometimes implicitly. A preference model that poorly describes how humans generate preferences risks learning a poor approximation of the human’s unobservable reward function. In ... | Reject | 4 | [
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"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "This paper focuses on reducing the gap between actual human behavior and ... | https://openreview.net/forum?id=mDEYl0Ucgr | 2501.06416 | papers/mDEYl0Ucgr.pdf | 604b567c1eb9a349480d52bd84748c6223e961da6757b89cb9066528ff9d4c68 | 4,269,937 | openreview | https://github.com/Stephanehk/InfluencingHumanPrefs | Stephanehk/InfluencingHumanPrefs | 54ad2720c1d5638574fc5d788a53008aef2c1c12 | repos/mDEYl0Ucgr.zip | d8288df4964091f57cabcce041c6af1cde1e0219d53c01654584bcef7248a4ce | 480,186 | 47 | {
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} | false | 2024-12-30T06:25:37 | {
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idpV2AqusC | 2,024 | rejected | Improving SAM Requires Rethinking its Optimization Formulation | [
"Wanyun Xie",
"Fabian Latorre",
"Kimon Antonakopoulos",
"Thomas Pethick",
"Volkan Cevher"
] | [
"~Wanyun_Xie1",
"~Fabian_Latorre1",
"~Kimon_Antonakopoulos1",
"~Thomas_Pethick1",
"~Volkan_Cevher1"
] | OpenReview API | This paper rethinks Sharpness-Aware Minimization (SAM), which is originally formulated as a zero-sum game where the weights of a network and a bounded perturbation try to minimize/maximize, respectively, the same differentiable loss. We argue that SAM should instead be reformulated using the 0-1 loss, as this provides ... | Reject | 4 | [
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"rating": "10: strong accept, should be highlighted at the conference",
"soundness": "4 excellent",
"presentation": "4 excellent",
"contribution": "4 excellent",
"confidence"... | https://openreview.net/forum?id=idpV2AqusC | 2407.12993 | papers/idpV2AqusC.pdf | 043920848e62cae574048dcad12a84129687839c6117ce8fb755c2ff31f6f07a | 558,257 | openreview | https://github.com/LIONS-EPFL/BiSAM | LIONS-EPFL/BiSAM | c6595703430d062f36d9f111c10d7be44b0fb22e | repos/idpV2AqusC.zip | ab1beb6d0b704f0ba877699e997800cab6e64a79a38143fe519cf2c0e3223753 | 449,352 | 17 | {
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} | 442 | {
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} | false | 2026-08-28T13:56:51 | {
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wysXxmukfCA | 2,023 | rejected | Towards Robust Model Watermark via Reducing Parametric Vulnerability | [
"Guanhao Gan",
"Yiming Li",
"Dongxian Wu",
"Shu-Tao Xia"
] | [
"~Guanhao_Gan1",
"~Yiming_Li1",
"~Dongxian_Wu1",
"~Shu-Tao_Xia1"
] | OpenReview API | Deep neural networks are valuable assets considering their commercial benefits and huge demands for costly annotation and computation resources. To protect the copyright of these deep models, backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model by embedding a ... | Reject | null | 3 | [
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} | 1,222 | {
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} | false | 2024-06-03T14:53:27 | {
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JmU7lyDxTpc | 2,022 | rejected | Multi-scale Feature Learning Dynamics: Insights for Double Descent | [
"Mohammad Pezeshki",
"Amartya Mitra",
"Yoshua Bengio",
"Guillaume Lajoie"
] | [
"~Mohammad_Pezeshki1",
"~Amartya_Mitra1",
"~Yoshua_Bengio1",
"~Guillaume_Lajoie1"
] | OpenReview API | A key challenge in building theoretical foundations for deep learning is the complex optimization dynamics of neural networks, resulting from the high-dimensional interactions between the large number of network parameters. Such non-trivial interactions lead to intriguing model behaviors such as the phenomenon of "dou... | Reject | null | 3 | [
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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=JmU7lyDxTpc | 2112.03215 | papers/JmU7lyDxTpc.pdf | d0d734b323f9a08c5b8c9d5ff07245f68e13ac91ea1a3590e45fb3d6a446ed3f | 2,529,304 | openreview | https://github.com/NNdoubledescent/doubledescent | NNdoubledescent/doubledescent | 3e2ef3e1247462824df8057db2605ef5519a0525 | repos/JmU7lyDxTpc.zip | c32570ed33c779ee82bb6d7498f58195d4c8813a651b16f4119ede88dfb73702 | 5,544,380 | 13 | {
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} | 5,414 | {
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} | false | 2021-11-29T20:24:16 | {
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N9oPAFcuYWX | 2,021 | rejected | Understanding and Mitigating Accuracy Disparity in Regression | [
"Jianfeng Chi",
"Han Zhao",
"Geoff Gordon",
"Yuan Tian"
] | [
"~Jianfeng_Chi1",
"~Han_Zhao1",
"~Geoff_Gordon2",
"~Yuan_Tian2"
] | OpenReview API | With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity on prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitig... | Reject | null | 4 | [
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"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=N9oPAFcuYWX | 2102.12013 | papers/N9oPAFcuYWX.pdf | 5abb64976ef9f17c2070ed1a7233127d981c7d16325765f47e46466e62e395c0 | 531,827 | openreview | https://github.com/JFChi/Understanding-and-Mitigating-Accuracy-Disparity-in-Regression | JFChi/Understanding-and-Mitigating-Accuracy-Disparity-in-Regression | 30bd1c82a5ccbcb84a23eb12da9071596796283b | repos/N9oPAFcuYWX.zip | 912385ce8d3e776d18c76e46e45ec6fa50ec8c35b3a68525dff02b25892419c4 | 3,045,097 | 12 | {
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} | 3,317 | {
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} | false | 2021-12-29T21:48:21 | {
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IgrLJslvxa | 2,025 | rejected | PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning | [
"Tingchen Fu",
"Mrinank Sharma",
"Philip Torr",
"Yonadav G Shavit",
"Shay B Cohen",
"David Krueger",
"Fazl Barez"
] | [
"~Tingchen_Fu1",
"~Mrinank_Sharma1",
"~Philip_Torr1",
"~Yonadav_G_Shavit1",
"~Shay_B_Cohen1",
"~David_Krueger1",
"~Fazl_Barez1"
] | OpenReview API | Preference learning is a central component for aligning current LLMs, but this process can be vulnerable to data poisoning attacks. To address this concern, we introduce PoisonBench, a benchmark for evaluating large language models' susceptibility to data poisoning during preference learning. Data poisoning attacks can... | Reject | 6 | [
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"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper presents POISONBENCH, a benchmark designed to assess the vulner... | https://openreview.net/forum?id=IgrLJslvxa | 2410.08811 | papers/IgrLJslvxa.pdf | 6a1b8cfb2f98a2fe76c0be3ce895402e97f740527d02ff266aeaabfcc1f7ecd4 | 847,280 | openreview | https://github.com/TingchenFu/PoisonBench | TingchenFu/PoisonBench | 61096ab8b740e782a666f365bd0003c98660dc41 | repos/IgrLJslvxa.zip | 60f8341ead2f2e0eb646be8fbec23d684cb1e0de9de11a1e9e66192fc623f925 | 409,770 | 10 | {
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} | 406 | {
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} | false | 2024-10-19T19:04:15 | {
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WO4BCqEyWc | 2,024 | rejected | Augmentation-aware Self-Supervised Learning with Conditioned Projector | [
"Marcin Przewięźlikowski",
"Mateusz Pyla",
"Bartosz Michał Zieliński",
"Bartłomiej Twardowski",
"Jacek Tabor",
"Marek Śmieja"
] | [
"~Marcin_Przewięźlikowski1",
"~Mateusz_Pyla1",
"~Bartosz_Michał_Zieliński1",
"~Bartłomiej_Twardowski1",
"~Jacek_Tabor1",
"~Marek_Śmieja1"
] | OpenReview API | Self-supervised learning (SSL) is a powerful technique for learning robust representations from unlabeled data. By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches. However, this invariance may be harmful to solving s... | Reject | 4 | [
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"rating": "3: reject, not good enough",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "5: You are absolutely certain about your ass... | https://openreview.net/forum?id=WO4BCqEyWc | 2306.06082 | papers/WO4BCqEyWc.pdf | 8e00967f8258c9acb8baa2a1e2df372d1fe052f741e6e0519e618828ed555c68 | 6,528,454 | openreview | https://github.com/gmum/CASSLE | gmum/CASSLE | 6e345133c7525644f2dd98cf9324b55d0014fbde | repos/WO4BCqEyWc.zip | 5ea97108385012ef2db88d8dd18e5ab33dd2166d8d29575861784873ae50441f | 356,973 | 20 | {
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} | 470 | {
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} | false | 2026-07-16T08:52:45 | {
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vNrmEgfGIg3 | 2,023 | rejected | Filtered Semi-Markov CRF | [
"Urchade Zaratiana",
"Nadi Tomeh",
"Niama Elkhbir",
"Pierre Holat",
"Thierry Charnois"
] | [
"~Urchade_Zaratiana1",
"~Nadi_Tomeh1",
"~Niama_Elkhbir1",
"~Pierre_Holat1",
"~Thierry_Charnois2"
] | OpenReview API | Semi-Markov CRF \citep{semicrf} has been proposed as an alternative to the traditional Linear Chain CRF\citep{crf} for text segmentation tasks such as Named Entity Recognition. In contrast to CRF, which treats text segmentation as token-level prediction, Semi-CRF considers spans as the task's basic unit, which makes it... | Reject | null | 3 | [
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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.",
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} | 1,223 | {
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} | false | 2024-01-05T19:48:13 | {
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X3WxnuzAYyE | 2,022 | rejected | PKCAM: Previous Knowledge Channel Attention Module | [
"Eslam Mohamed BAKR",
"Ahmad A. Al Sallab",
"Mohsen Rashwan"
] | [
"~Eslam_Mohamed_BAKR1",
"~Ahmad_A._Al_Sallab1",
"~Mohsen_Rashwan1"
] | OpenReview API | Attention mechanisms have been explored with CNNs, both across the spatial and channel dimensions.
However, all the existing methods devote the attention modules to capture local interactions from the current feature map only, disregarded the valuable previous knowledge that is acquired by the earlier layers.
This pa... | 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=X3WxnuzAYyE | 2211.07521 | papers/X3WxnuzAYyE.pdf | 99fb3ad6524e4930e8663b5ecf321a061f4fd50035253a0fc3a41163075674d4 | 1,128,222 | openreview | https://github.com/eslambakr/EMCA | eslambakr/EMCA | 76bae427ea2f66979aed6a9dcef5e84ed922d14e | repos/X3WxnuzAYyE.zip | d62d9ceb8af92d4b877636f76f7f7a63d1cfef19891d0dde257f65ccc6e294f6 | 2,366,697 | 28 | {
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} | 5,864 | {
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} | false | 2021-11-24T18:02:44 | {
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E4PK0rg2eP | 2,021 | rejected | Parameter-Efficient Transfer Learning with Diff Pruning | [
"Demi Guo",
"Alexander M Rush",
"Yoon Kim"
] | [
"~Demi_Guo1",
"~Alexander_M_Rush1",
"~Yoon_Kim1"
] | OpenReview API | While task-specific finetuning of deep networks pretrained with self-supervision has led to significant empirical advances in NLP, their large size makes the standard finetuning approach difficult to apply to multi-task, memory-constrained settings, as storing the full model parameters for each task become prohibitivel... | Reject | null | 4 | [
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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",
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"soun... | https://openreview.net/forum?id=E4PK0rg2eP | 2012.07463 | papers/E4PK0rg2eP.pdf | a3966e58b21caeca135ff1ac346dd6e1587ca0c648c5fb61992cc59cfecb155d | 424,130 | openreview | https://github.com/dguo98/DiffPruning | dguo98/DiffPruning | 085fa7d9e2bcbcd19f705a08b1c0c6c74c54bada | repos/E4PK0rg2eP.zip | f11d18a2726c04ebd656b7d948cd42ba78b6ac11635e7c6383e40ac4dbf32af5 | 6,843,736 | 471 | {
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ZPZ4eCQU9k | 2,025 | rejected | xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories | [
"Maurice Kraus",
"Felix Divo",
"Devendra Singh Dhami",
"Kristian Kersting"
] | [
"~Maurice_Kraus1",
"~Felix_Divo1",
"~Devendra_Singh_Dhami1",
"~Kristian_Kersting1"
] | OpenReview API | Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal and multivariate components is crucial for reliable predictions.
We introduce xLSTM-Mixer, a model designed to effectively integrate temporal... | Reject | 5 | [
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"soundness": 2,
"presentation": 2,
"contribution": 2,
"confidence": 3,
"summary": "The paper introduces xLSTM-Mixer, a new model for forecasting complex tim... | https://openreview.net/forum?id=ZPZ4eCQU9k | 2410.16928 | papers/ZPZ4eCQU9k.pdf | 5f4b67ff8b6aec7b74f60e40a0cef8756d22cf79cfee7e2a3d46ef2ef045df1c | 587,538 | openreview | https://github.com/mauricekraus/xLSTM-Mixer | mauricekraus/xLSTM-Mixer | 730b0531aa9456e498765028f3c22ca3677de42e | repos/ZPZ4eCQU9k.zip | 02df029162721aa1433c38ddee9fed02f661ca724ef35e42e00623115a93abf8 | 434,103 | 88 | {
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PoBB8n52oi | 2,024 | rejected | SummaryMixing: A Linear-Complexity Alternative to Self-Attention for Speech Recognition and Understanding | [
"Titouan Parcollet",
"Rogier van Dalen",
"Shucong Zhang",
"Sourav Bhattacharya"
] | [
"~Titouan_Parcollet1",
"~Rogier_van_Dalen2",
"~Shucong_Zhang2",
"~Sourav_Bhattacharya1"
] | OpenReview API | Modern speech processing systems rely on self-attention. Unfortunately, token
mixing with self-attention takes quadratic time in the length of the speech utterance,
slowing down inference as well as training and increasing memory consumption.
Cheaper alternatives to self-attention for ASR have been developed, but they ... | Reject | 4 | [
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],
"rating": "3: reject, not good enough",
"soundness": "3 good",
"presentation": "2 fair",
"contribution": "3 good",
"confidence": "5: You are absolutely certain about your ass... | https://openreview.net/forum?id=PoBB8n52oi | 2307.07421 | papers/PoBB8n52oi.pdf | 83e672dec3700172524e1d3c3deb24b78e26f07578abc6a132c6ec2bf6c4d83e | 293,159 | openreview | https://github.com/SamsungLabs/SummaryMixing | SamsungLabs/SummaryMixing | d1b1f425149cea28f0e0318de82e525af2523b70 | repos/PoBB8n52oi.zip | d534a7932c656f9138cd44286daf34eca03cb6ad206a64e31688a5439f84c857 | 460,239 | 7 | {
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} | 491 | {
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} | false | 2025-06-24T09:23:38 | {
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me09xlTmm8 | 2,023 | rejected | Transport with Support: Data-Conditional Diffusion Bridges | [
"Ella Tamir",
"Martin Trapp",
"Arno Solin"
] | [
"~Ella_Tamir1",
"~Martin_Trapp2",
"~Arno_Solin1"
] | OpenReview API | The dynamic Schrödinger bridge problem provides an appealing setting for posing optimal transport problems as learning non-linear diffusion processes and enables efficient iterative solvers. Recent works have demonstrated state-of-the-art results (eg, in modelling single-cell embryo RNA sequences or sampling from compl... | 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=me09xlTmm8 | 2301.13636 | papers/me09xlTmm8.pdf | 1ace3aef22405df3a03de89bcbab28e1ab9a95ffbba75c4075fafc332c8b0d10 | 3,063,866 | openreview | https://github.com/AaltoML/iterative-smoothing-bridge | AaltoML/iterative-smoothing-bridge | 12263a6f0be10786e4ff05c567fdc5b750f1b297 | repos/me09xlTmm8.zip | 170804791aea8d023588f021026982e977918238411f1401853615ee7139ccbf | 1,299,216 | 31 | {
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} | 1,225 | {
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} | false | 2023-11-24T08:58:52 | {
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dHJtoaE3yRP | 2,022 | rejected | NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning | [
"Wentao Zhang",
"Zeang Sheng",
"Mingyu Yang",
"Yang Li",
"Yu Shen",
"Zhi Yang",
"Zichao Yang",
"Bin CUI"
] | [
"~Wentao_Zhang1",
"~Zeang_Sheng1",
"~Mingyu_Yang2",
"~Yang_Li36",
"~Yu_Shen3",
"~Zhi_Yang4",
"~Zichao_Yang1",
"~Bin_CUI2"
] | OpenReview API | Recently, graph neural networks (GNNs) have shown prominent performance in graph representation learning by leveraging knowledge from both graph structure and node features. However, most of them have two major limitations. First, GNNs can learn higher-order structural information by stacking more layers but can not de... | Reject | null | 3 | [
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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=dHJtoaE3yRP | 2206.08583 | papers/dHJtoaE3yRP.pdf | 5be907ba1067a1c11b4ec9d2370f2356011d2955424006bd5f54c294e0de35ab | 1,524,273 | openreview | https://github.com/PKU-DAIR/NAFS | PKU-DAIR/NAFS | 966cc58a1df85f2179f69f9785c00cf572f0c121 | repos/dHJtoaE3yRP.zip | d34bf65108da728217f64bd47eaa34ae2786848d9b8fa033db0e36a639bb067e | 5,710,826 | 11 | {
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} | 5,879 | {
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} | false | 2022-06-20T02:49:28 | {
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Jq8JGA89sDa | 2,021 | rejected | Detecting Hallucinated Content in Conditional Neural Sequence Generation | [
"Chunting Zhou",
"Jiatao Gu",
"Mona T. Diab",
"Paco Guzmán",
"Luke Zettlemoyer",
"Marjan Ghazvininejad"
] | [
"~Chunting_Zhou1",
"~Jiatao_Gu1",
"~Mona_T._Diab1",
"fguzman@fb.com",
"~Luke_Zettlemoyer1",
"~Marjan_Ghazvininejad1"
] | OpenReview API | Neural sequence models can generate highly fluent sentences but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input, which can cause a lack of trust in the model.
To better assess the faithfulness of the machine outputs, we propose a new task to predict w... | 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": "",
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} | false | 2022-04-15T00:52:31 | {
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RfrdbJVvVf | 2,025 | rejected | MatMamba: A Matryoshka State Space Model | [
"Abhinav Shukla",
"Sai Vemprala",
"Aditya Kusupati",
"Ashish Kapoor"
] | [
"~Abhinav_Shukla1",
"~Sai_Vemprala1",
"~Aditya_Kusupati1",
"~Ashish_Kapoor1"
] | OpenReview API | State Space Models (SSMs) like Mamba2 are a promising alternative to Transformers, with faster theoretical training and inference times -- especially for long context lengths. Recent work on Matryoshka Representation Learning -- and its application to Transformer backbones in works like MatFormer -- showed how to intr... | Reject | 4 | [
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"rating": 6,
"soundness": 4,
"presentation": 4,
"contribution": 3,
"confidence": 4,
"summary": "This work extends Matryoshka Representation Learning to Mamba2, a represen... | https://openreview.net/forum?id=RfrdbJVvVf | 2410.06718 | papers/RfrdbJVvVf.pdf | 97bf99cb9e07b4ffc98779861f48251b0230e99179dd8dab3948c505c32c5043 | 653,017 | openreview | https://github.com/GenRobo/MatMamba | GenRobo/MatMamba | 3233d4c7e7652ec13f203eb7b03f339c181660f6 | repos/RfrdbJVvVf.zip | f711545a72879fdb8851900a919098262fa920a9e88beceb643330d81858c844 | 381,431 | 21 | {
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} | 408 | {
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} | false | 2024-11-21T16:45:50 | {
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fH9eqpCcR3 | 2,024 | rejected | Multiple Physics Pretraining for Physical Surrogate Models | [
"Michael McCabe",
"Bruno Régaldo-Saint Blancard",
"Liam Holden Parker",
"Ruben Ohana",
"Miles Cranmer",
"Alberto Bietti",
"Michael Eickenberg",
"Siavash Golkar",
"Geraud Krawezik",
"Francois Lanusse",
"Mariel Pettee",
"Tiberiu Tesileanu",
"Kyunghyun Cho",
"Shirley Ho"
] | [
"~Michael_McCabe2",
"~Bruno_Régaldo-Saint_Blancard1",
"~Liam_Holden_Parker1",
"~Ruben_Ohana1",
"~Miles_Cranmer2",
"~Alberto_Bietti1",
"~Michael_Eickenberg5",
"~Siavash_Golkar1",
"gkrawezik@flatironinstitute.org",
"~Francois_Lanusse2",
"~Mariel_Pettee1",
"~Tiberiu_Tesileanu1",
"~Kyunghyun_Cho... | OpenReview API | We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling. MPP involves training large surrogate models to predict the dynamics of multiple heterogeneous physical systems simultaneously by learning features that are broadly useful across divers... | Reject | 5 | [
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"rating": "6: marginally above the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=fH9eqpCcR3 | 2310.02994 | papers/fH9eqpCcR3.pdf | 01f4ed804cd79222738933baacfeabf298df378d1de69f75c675cbcdc2dbae83 | 3,414,080 | openreview | https://github.com/PolymathicAI/multiple_physics_pretraining | PolymathicAI/multiple_physics_pretraining | e751fc25ae5274c3ddc869bf73afd6fcd4163cb5 | repos/fH9eqpCcR3.zip | 8e05c07948bb5c1375c139da4fe39e90043a16db99ea08345f870d315f87d7e6 | 458,749 | 13 | {
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} | 495 | {
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} | false | 2024-12-06T15:43:29 | {
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} | {
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3leZITnUE9r | 2,023 | rejected | An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models | [
"Saghar Hosseini",
"Ahmed Hassan Awadallah",
"Hamid Palangi"
] | [
"~Saghar_Hosseini1",
"~Ahmed_Hassan_Awadallah1",
"~Hamid_Palangi1"
] | OpenReview API | Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents. In this paper, we leverage the primary task of PTLMs, i.e. language modeling, and propose a new metric to quantify manifested implicit representational harms in PTL... | 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=3leZITnUE9r | 2301.09211 | papers/3leZITnUE9r.pdf | 111fd283ddaa801cffd2f85b56deae23dff3c7e54620549ca4b4184b1c9f4663 | 453,389 | openreview | https://github.com/microsoft/SafeNLP | microsoft/SafeNLP | e351b6cb892284cf843b88e363b1a6dbaded584e | repos/3leZITnUE9r.zip | caa7a49a41212bc290f462894c1d69d61a9743bbae58bea2f9ac055a6a0bd397 | 1,698,691 | 3 | {
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} | 1,254 | {
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} | true | 2023-10-18T20:57:47 | {
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FD8xldQIgdq | 2,022 | rejected | Robust Models Are More Interpretable Because Attributions Look Normal | [
"Zifan Wang",
"Matt Fredrikson",
"Anupam Datta"
] | [
"~Zifan_Wang1",
"~Matt_Fredrikson1",
"~Anupam_Datta1"
] | OpenReview API | Recent work has found that adversarially-robust deep networks used for image classification are more interpretable: their feature attributions tend to be sharper, and are more concentrated on the objects associated with the image's ground-truth class. We show that smooth decision boundaries play an important role in th... | 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=FD8xldQIgdq | 2103.11257 | papers/FD8xldQIgdq.pdf | b45d21710e26232a8d2442332c71d84584d7baae403893002c0134d92079f124 | 25,874,427 | openreview | https://github.com/zifanw/boundary | zifanw/boundary | a901b7e75ee8cdf683c490ba076a9308301db2b9 | repos/FD8xldQIgdq.zip | 0ebf7a00af4ba389f96dca33844c234c254a89f1a46a19ee0ce0f0393a98de6c | 6,457,814 | 8 | {
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} | 6,321 | {
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} | false | 2021-08-13T20:28:44 | {
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SnhmiKUPWL | 2,021 | rejected | Leveraging Class Hierarchies with Metric-Guided Prototype Learning | [
"Vivien Sainte Fare Garnot",
"Loic Landrieu"
] | [
"~Vivien_Sainte_Fare_Garnot1",
"~Loic_Landrieu1"
] | OpenReview API | In many classification tasks, the set of classes can be organized according to a meaningful hierarchy. This structure can be used to assess the severity of confusing each pair of classes, and summarized under the form of a cost matrix which also defines a finite metric. We propose to integrate this metric in the super... | Reject | null | 4 | [
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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=SnhmiKUPWL | 2007.03047 | papers/SnhmiKUPWL.pdf | 2ef02da2fb1d48418933787b103bd90527527938a3d2b97b645fb320f40463df | 1,110,853 | openreview | https://github.com/VSainteuf/metric-guided-prototypes-pytorch | VSainteuf/metric-guided-prototypes-pytorch | fae90235b57a71f3ab682917eaf5517799fbffed | repos/SnhmiKUPWL.zip | 4fa50ce76fd27a5e61f291f897bfb9724e900ea39be93371509fa912754abf63 | 1,257,982 | 10 | {
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} | 4,169 | {
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} | false | 2021-10-20T11:30:19 | {
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74QmBTV0Zf | 2,025 | rejected | Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models | [
"Michael Günther",
"Isabelle Mohr",
"Daniel James Williams",
"Bo Wang",
"Han Xiao"
] | [
"~Michael_Günther1",
"~Isabelle_Mohr1",
"~Daniel_James_Williams1",
"~Bo_Wang31",
"~Han_Xiao8"
] | OpenReview API | Many use cases require retrieving smaller portions of text, and dense vector-based retrieval systems often perform better with shorter text segments, as the semantics are less likely to be "over-compressed" in the embeddings. Consequently, practitioners often split text documents into smaller chunks and encode them sep... | Reject | 4 | [
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"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "The paper introduces late chunking for document embeddings, which suggests... | https://openreview.net/forum?id=74QmBTV0Zf | 2409.04701 | papers/74QmBTV0Zf.pdf | 7f40499529248f5d920fb3d021e728b68c17c8c5cb8219426f908bb01d742c1b | 435,456 | openreview | https://github.com/jina-ai/late-chunking | jina-ai/late-chunking | 1d3bb02bf091becd0771455e4e7959463935e26c | repos/74QmBTV0Zf.zip | 0003f6c5611ca3e991f1d3d284e179f5c87f66db7110df1868248973ff5159b0 | 381,516 | 12 | {
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} | 421 | {
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} | false | 2024-12-23T13:38:26 | {
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gusHSc09zj | 2,024 | rejected | Discovering Mixtures of Structural Causal Models from Time Series Data | [
"Sumanth Varambally",
"Yian Ma",
"Rose Yu"
] | [
"~Sumanth_Varambally1",
"~Yian_Ma1",
"~Rose_Yu1"
] | OpenReview API | In fields such as finance, climate science, and neuroscience, inferring causal relationships from time series data poses a formidable challenge. While contemporary techniques can handle non-linear relationships between variables and flexible noise distributions, they rely on the simplifying assumption that data origina... | Reject | 4 | [
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],
"rating": "5: marginally below the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "3: You are fairly confiden... | https://openreview.net/forum?id=gusHSc09zj | 2310.06312 | papers/gusHSc09zj.pdf | a1653ffb9495cfc06f9376bc1a39359d2396e43200e55f806d03b5ebdcdef87b | 918,134 | openreview | https://github.com/Rose-STL-Lab/MCD | Rose-STL-Lab/MCD | a9de419a6293b34d43b7519621a100dda51fc5a3 | repos/gusHSc09zj.zip | 1c3d37399f2867c53e5d954df863f49646d4a8786c50928580161958835f1737 | 511,566 | 49 | {
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} | 500 | {
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} | false | 2025-06-26T01:59:50 | {
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} | |
_AkC4QYxF5 | 2,023 | rejected | Closing the Gap Between SVRG and TD-SVRG with Gradient Splitting | [
"Arsenii Mustafin",
"Ioannis Paschalidis",
"Alex Olshevsky"
] | [
"~Arsenii_Mustafin1",
"~Ioannis_Paschalidis1",
"~Alex_Olshevsky1"
] | OpenReview API | Temporal difference (TD) learning is a simple algorithm for policy evaluation
in reinforcement learning. The performance of TD learning is affected by high
variance and it can be naturally enhanced with variance reduction techniques, such
as the Stochastic Variance Reduced Gradient (SVRG) method. Recently, multiple
wor... | 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=_AkC4QYxF5 | 2211.16237 | papers/_AkC4QYxF5.pdf | f73f4731b2cfd3962e1983dec269f68dfae7e7a21dd16cae2479c1ac8ec1355f | 552,054 | openreview | https://github.com/gaarsmu/SVRG_for_TD_learning | gaarsmu/SVRG_for_TD_learning | 0f8ddee6a59e541254bb1a80463825b10ce11762 | repos/_AkC4QYxF5.zip | b145a42e75033fd203b05618fd6ad99f7373785f1dcafc72894402a85bb9e957 | 555,452 | 17 | {
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} | 1,279 | {
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} | false | 2023-03-19T15:20:21 | {
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} | {
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} |
jxTRL-VOoQo | 2,022 | rejected | Evaluating Deep Graph Neural Networks | [
"Wentao Zhang",
"Zeang Sheng",
"Jiang Yuezihan",
"Yikuan Xia",
"Jun Gao",
"Zhi Yang",
"Bin CUI"
] | [
"~Wentao_Zhang1",
"~Zeang_Sheng1",
"~Jiang_Yuezihan1",
"~Yikuan_Xia1",
"~Jun_Gao6",
"~Zhi_Yang4",
"~Bin_CUI2"
] | OpenReview API | Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks. However, most GNNs only have shallow architectures, which limits performance improvement. In this paper, we conduct a systematic experimental evaluation on the fundamental limitations of current architecture designs. Based on t... | Reject | null | 4 | [
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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.",
"recommendati... | https://openreview.net/forum?id=jxTRL-VOoQo | 2108.00955 | papers/jxTRL-VOoQo.pdf | 5ec0879f24472d76bd525ec41441a0c1ed47d95c40cc32bb48ee784c5db14370 | 788,758 | openreview | https://github.com/zwt233/AIR | zwt233/AIR | d5cc2325b071ad062c7a7855f6a42336d21a7c3d | repos/jxTRL-VOoQo.zip | 42fcf9644b138ac0ad9f6121d2aaa59e85dc982771548cf4b212c6a1e718f3f8 | 5,414,890 | 14 | {
".py": 14
} | 6,351 | {
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} | false | 2022-09-26T03:30:59 | {
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} | {
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iEcqwosBEgx | 2,021 | rejected | Novel Policy Seeking with Constrained Optimization | [
"Hao Sun",
"Zhenghao Peng",
"Bo Dai",
"Jian Guo",
"Dahua Lin",
"Bolei Zhou"
] | [
"~Hao_Sun3",
"~Zhenghao_Peng1",
"~Bo_Dai2",
"guoj@pcl.ac.cn",
"~Dahua_Lin1",
"~Bolei_Zhou5"
] | OpenReview API | We address the problem of seeking novel policies in reinforcement learning tasks. Instead of following the multi-objective framework commonly used in existing methods, we propose to rethink the problem under a novel perspective of constrained optimization. We at first introduce a new metric to evaluate the difference b... | Reject | null | 4 | [
{
"id": "jOp2T9iGhp",
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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": "",
"sound... | https://openreview.net/forum?id=iEcqwosBEgx | 2005.10696 | papers/iEcqwosBEgx.pdf | 6ea4d66dde148372a8c31d0edd502732266d6e6adc5568bc4a7476c321853d8b | 5,812,241 | openreview | https://github.com/holarissun/NPSCO | holarissun/NPSCO | 28106089d65e392746ece741a735510c96eb65f9 | repos/iEcqwosBEgx.zip | f61b43633dd0461c9accf1268ddbc5a0f8024a5d4062c5d4be330fc3423ba2c9 | 4,463,029 | 5 | {
".py": 5
} | 4,357 | {
"Python": 119589
} | false | 2020-08-11T03:09:48 | {
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} | {
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} |
I1VCj1l1Zn | 2,025 | rejected | DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language Models | [
"Yuxuan Zhang",
"Ruizhe Li"
] | [
"~Yuxuan_Zhang15",
"~Ruizhe_Li2"
] | OpenReview API | Recent advancements in Large Language Models (LLMs) have achieved robust performance across diverse tasks, but fine-tuning these models for specific domains remains resource-intensive. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) address this challenge by fine-tuning a small subset of ... | Reject | 3 | [
{
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"ICLR.cc/2025/Conference/Submission13516/Reviewer_4fCq"
],
"rating": 3,
"soundness": 1,
"presentation": 2,
"contribution": 1,
"confidence": 3,
"summary": "This paper proposes a method to dynamically fuse pre-trained task-specifi... | https://openreview.net/forum?id=I1VCj1l1Zn | 2410.01497 | papers/I1VCj1l1Zn.pdf | 8323c066f332b87ab0dc5e6680565091367367241c598687e772f2fe98e167a6 | 2,722,444 | openreview | https://github.com/MeCuping/DLP-LoRA | MeCuping/DLP-LoRA | d68fd38f36d9762f3e60041472193d379257cfb9 | repos/I1VCj1l1Zn.zip | d7af434a61dce55ca98f67239b40ba01a2c39a8f2e4f1b72d8ac136ac51f5a84 | 462,891 | 18 | {
".py": 18
} | 426 | {
"Python": 62697,
"Batchfile": 28
} | false | 2024-10-03T01:48:29 | {
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} | {
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} | |
96nX9xIIx2 | 2,024 | rejected | Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective | [
"Can Jin",
"Tianjin Huang",
"Yihua Zhang",
"Mykola Pechenizkiy",
"Sijia Liu",
"Shiwei Liu",
"Tianlong Chen"
] | [
"~Can_Jin1",
"~Tianjin_Huang1",
"~Yihua_Zhang1",
"~Mykola_Pechenizkiy1",
"~Sijia_Liu1",
"~Shiwei_Liu2",
"~Tianlong_Chen1"
] | OpenReview API | The rapid development of large-scale deep learning models questions the affordability of hardware platforms, which necessitates the pruning to reduce their computational and memory footprints. Sparse neural networks as the product, have demonstrated numerous favorable benefits like low complexity, undamaged generalizat... | Reject | 4 | [
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],
"rating": "5: marginally below the acceptance threshold",
"soundness": "3 good",
"presentation": "4 excellent",
"contribution": "3 good",
"confidence": "4: You are confident ... | https://openreview.net/forum?id=96nX9xIIx2 | 2312.01397 | papers/96nX9xIIx2.pdf | 56d73c5360d9ba99f924049be362260485caf6bcf5ffdd2e9b8c63a2353e7ceb | 4,569,192 | openreview | https://github.com/UNITES-Lab/VPNs | UNITES-Lab/VPNs | 912072c4ec2156a7d0414f00c463f3bd48279ab5 | repos/96nX9xIIx2.zip | 0a3ffaa4fa693c4cf0b2fb82907aeb748c395973f623e242209ea07fe400df99 | 501,483 | 9 | {
".py": 6,
".sh": 3
} | 500 | {
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} | false | 2023-12-05T02:36:39 | {
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} | {
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} | |
0DwzMsUNIr | 2,023 | rejected | From Points to Functions: Infinite-dimensional Representations in Diffusion Models | [
"Sarthak Mittal",
"Guillaume Lajoie",
"Stefan Bauer",
"Arash Mehrjou"
] | [
"~Sarthak_Mittal1",
"~Guillaume_Lajoie1",
"~Stefan_Bauer1",
"~Arash_Mehrjou1"
] | OpenReview API | Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative Adversarial Networks (GANs) or the decoder of Variational Autoencoders (VAEs) which produce samples from the target distribution in a single step. Thus, in diffusion models every ... | Reject | null | 4 | [
{
"id": "IezE10DPA9",
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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=0DwzMsUNIr | 2210.13774 | papers/0DwzMsUNIr.pdf | 89b6b10c9c3512e38d1e040c81c307cecc37f331200fe531c134311659914ef5 | 3,225,151 | openreview | https://github.com/sarthmit/traj_drl | sarthmit/traj_drl | 61aef79dfa4d88dcb6f91c5e80ebc6e91e8df481 | repos/0DwzMsUNIr.zip | dd80fa7b82867a4ae01b6e8788e8d6b5f841241ccaaac054ef41c03d98fbeaa1 | 1,422,515 | 113 | {
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".cpp": 2,
".cu": 2
} | 1,302 | {
"Python": 494902,
"Cuda": 14488,
"Shell": 7432,
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} | false | 2022-10-25T14:48:48 | {
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} | {
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} |
hq7vLjZTJPk | 2,022 | rejected | A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks | [
"Chunyang Liao",
"Zhenxun Zhuang",
"Mingrui Liu"
] | [
"~Chunyang_Liao1",
"~Zhenxun_Zhuang1",
"~Mingrui_Liu2"
] | OpenReview API | In distributed training of deep neural networks or Federated Learning (FL), people usually run Stochastic Gradient Descent (SGD) or its variants on each machine and communicate with other machines periodically. However, SGD might converge slowly in training some deep neural networks (e.g., RNN, LSTM) because of the exp... | Reject | null | 4 | [
{
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"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=hq7vLjZTJPk | 2205.05040 | papers/hq7vLjZTJPk.pdf | 5b9ba9370cbffa344aada27734a1f8d5f515c13c1ecf440b8c17bcf2119fd204 | 3,780,791 | openreview | https://github.com/MingruiLiu-ML-Lab/Communication-Efficient-Local-Gradient-Clipping | MingruiLiu-ML-Lab/Communication-Efficient-Local-Gradient-Clipping | 5f572e368492acc30732dbf47121761de70aacf1 | repos/hq7vLjZTJPk.zip | b0b0f08554e5f4dfdaa8bd01b91538cbe0feafdb7c17467ec1d0471be2382a24 | 6,518,207 | 26 | {
".py": 20,
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} | 6,368 | {
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} | false | 2022-09-21T16:12:17 | {
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} | {
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PGmqOzKEPZN | 2,021 | rejected | Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation | [
"Masahiro Kato",
"Takeshi Teshima"
] | [
"~Masahiro_Kato1",
"~Takeshi_Teshima1"
] | OpenReview API | The estimation of the ratio of two probability densities has garnered attention as the density ratio is useful in various machine learning tasks, such as anomaly detection and domain adaptation. To estimate the density ratio, methods collectively known as direct density ratio estimation (DRE) have been explored. These ... | Reject | null | 4 | [
{
"id": "mYz0sYiGePz",
"reviewer_signature": [
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],
"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 ... | https://openreview.net/forum?id=PGmqOzKEPZN | 2006.06979 | papers/PGmqOzKEPZN.pdf | 45cab1b2e9f942bbf8b96da30a2eca81b84ca95333cd3fa4f07961595057cdc9 | 4,267,784 | openreview | https://github.com/MasaKat0/D3RE | MasaKat0/D3RE | fa008291ec2b30654081f8c80c0b682fe5df1f76 | repos/PGmqOzKEPZN.zip | c0eb5c7feb70334b25bae3d11ddf9219317c9274cba90b5fde7e5e4346d1c823 | 7,703,983 | 90 | {
".py": 79,
".sh": 11
} | 4,639 | {
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} | false | 2022-11-29T19:56:39 | {
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996aKQIom0 | 2,025 | rejected | PingPong: A Benchmark for Role-Playing Language Models with User Emulation and Multi-Model Evaluation | [
"Ilya Gusev"
] | [
"~Ilya_Gusev1"
] | OpenReview API | We introduce a benchmark for evaluating the role-playing capabilities of language models. Our approach leverages language models themselves to emulate users in dynamic, multi-turn conversations and to assess the resulting dialogues. The framework consists of three main components: a player model assuming a specific cha... | Reject | 6 | [
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"rating": 3,
"soundness": 2,
"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "The paper introduces PingPong a benchmark that aims to simulate and assess... | https://openreview.net/forum?id=996aKQIom0 | 2409.06820 | papers/996aKQIom0.pdf | 2ae8962d19fe1d7fc1fa37655a36874da21a01792ac9f62f5c7d95a688166ae5 | 1,389,322 | openreview | https://github.com/IlyaGusev/ping_pong_bench | IlyaGusev/ping_pong_bench | 4e3b0e0b6b093670f67b80d7d62b5a5e8e7b13fd | repos/996aKQIom0.zip | 820ead7e8ec5e0157c0ef08d3c83cbf1af0ecef7b8099a373d69aefc1577b0ee | 290,873 | 22 | {
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} | 429 | {
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} | false | 2025-05-25T20:17:29 | {
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} | {
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E296x0YpML | 2,024 | rejected | Fooling the Textual Fooler via Randomizing Latent Representations | [
"Duy Cao Hoang",
"Nguyen Hung-Quang",
"Saurav Manchanda",
"Minlong Peng",
"Kok-Seng Wong",
"Khoa D Doan"
] | [
"~Duy_Cao_Hoang1",
"~Nguyen_Hung-Quang1",
"~Saurav_Manchanda1",
"~Minlong_Peng1",
"~Kok-Seng_Wong1",
"~Khoa_D_Doan1"
] | OpenReview API | Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cause the models to misbehave. Among these attacks, adversarial word-level perturbations are well-studied and effective attack strategies. Thes... | Reject | 4 | [
{
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"rating": "5: marginally below the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "5: You are absolutely cert... | https://openreview.net/forum?id=E296x0YpML | 2310.01452 | papers/E296x0YpML.pdf | 81d777153b5ddf419736c45453d6df8d4c7e59ad199d5fe460677826182ac410 | 338,325 | openreview | https://github.com/mail-research/AdvFooler-text-defender | mail-research/AdvFooler-text-defender | 29b69d7f38f73ab65eea78467c993d0a4d0e3035 | repos/E296x0YpML.zip | af74518c78f6dd7e0aa9a6ef6724c328e01d8f44db0656063061444858b0f8ce | 613,774 | 282 | {
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} | 508 | {
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} | false | 2024-07-22T06:03:45 | {
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QP02DQ-FG-8 | 2,023 | rejected | Incomplete to complete multiphysics forecasting - a hybrid approach for learning unknown phenomena | [
"Nilam Nandkishor Tathawadekar",
"Nguyen Anh Khoa Doan",
"Camilo Fernando Silva",
"Nils Thuerey"
] | [
"~Nilam_Nandkishor_Tathawadekar2",
"~Nguyen_Anh_Khoa_Doan1",
"~Camilo_Fernando_Silva1",
"~Nils_Thuerey1"
] | OpenReview API | Modeling complex dynamical systems where only partial knowledge of their physical mechanisms is available is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and data, often fail to accurately simulate the evolution o... | 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 piece... | https://openreview.net/forum?id=QP02DQ-FG-8 | 2111.11185 | papers/QP02DQ-FG-8.pdf | ca651ffdcb1b721f03052392620772eb02244738ad2c859419391ace2c1e6524 | 2,072,512 | openreview | https://github.com/tum-pbs/Hybrid-Solver-for-Reactive-Flows | tum-pbs/Hybrid-Solver-for-Reactive-Flows | 75d836ff82c30da698b8abd12fe0e2c5b6d79da2 | repos/QP02DQ-FG-8.zip | 911e669bcbe2cd9192a255a8da0ff270896a0e4398746ca051b007d73ce96d2b | 1,410,465 | 31 | {
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} | 1,319 | {
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} | false | 2024-01-10T13:44:28 | {
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e_D6AmszH4P | 2,022 | rejected | ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure | [
"Felix Dangel",
"Lukas Tatzel",
"Philipp Hennig"
] | [
"~Felix_Dangel1",
"~Lukas_Tatzel1",
"~Philipp_Hennig1"
] | OpenReview API | Curvature in form of the Hessian or its generalized Gauss-Newton (GGN) approximation is valuable for algorithms that rely on a local model for the loss to train, compress, or explain deep networks. Existing methods based on implicit multiplication via automatic differentiation or Kronecker-factored block diagonal appro... | Reject | null | 4 | [
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} | 6,492 | {
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} | false | 2021-10-03T20:35:05 | {
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oWy06SBgt4 | 2,025 | rejected | 1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit | [
"Chang Gao",
"JingRen Hou",
"Kang Zhao",
"Jiaqi Wang",
"Jianfei Chen",
"Liping Jing"
] | [
"~Chang_Gao4",
"~JingRen_Hou1",
"~Kang_Zhao5",
"~Jiaqi_Wang8",
"~Jianfei_Chen1",
"~Liping_Jing3"
] | OpenReview API | Fully quantized training (FQT) accelerates the training of deep neural networks by quantizing the activations, weights, and gradients into lower precision. To explore the ultimate limit of FQT (the lowest achievable precision), we make a first attempt to 1-bit FQT. We provide a theoretical analysis of FQT based on Adam... | Reject | 4 | [
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"rating": 3,
"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 5,
"summary": "This paper explores the limit of fully quantized training (FQT) by propos... | https://openreview.net/forum?id=oWy06SBgt4 | 2408.14267 | papers/oWy06SBgt4.pdf | 8870b4a096c6d1b07e84f5c6f0c286ad92b11ada3914a34f25f7174abf611cc0 | 1,276,291 | openreview | https://github.com/Gaochang-bjtu/1-bit-FQT | Gaochang-bjtu/1-bit-FQT | 008b6188e4f1da2e6870f6c896afd89113ab2be4 | repos/oWy06SBgt4.zip | fab8dfa4ea68f187dd85a6eafda5599a7769cf65604d8d8b7b78779a0f8296ef | 490,626 | 63 | {
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} | 433 | {
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"Cuda": 11419,
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} | false | 2024-08-26T12:22:33 | {
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qud5pDnpzo | 2,024 | rejected | ViP: A Differentially Private Foundation Model for Computer Vision | [
"Yaodong Yu",
"Maziar Sanjabi",
"Yi Ma",
"Kamalika Chaudhuri",
"Chuan Guo"
] | [
"~Yaodong_Yu4",
"~Maziar_Sanjabi1",
"~Yi_Ma4",
"~Kamalika_Chaudhuri1",
"~Chuan_Guo1"
] | OpenReview API | Artificial intelligence (AI) has seen a tremendous surge in capabilities thanks to the use of foundation models trained on internet-scale data. On the flip side, the uncurated nature of internet-scale data also poses significant privacy and legal risks, as they often contain personal information or copyrighted material... | Reject | 3 | [
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"rating": "8: accept, good paper",
"soundness": "4 excellent",
"presentation": "4 excellent",
"contribution": "3 good",
"confidence": "4: You are confident in your assessment... | https://openreview.net/forum?id=qud5pDnpzo | 2306.08842 | papers/qud5pDnpzo.pdf | c80967407f7be8f70c7184b94dfe867c5017182668b760d8ffe295bef486e6ee | 1,619,764 | openreview | https://github.com/facebookresearch/ViP-MAE | facebookresearch/ViP-MAE | d87fd2de456a07f2c21d9017850448d1caffd3ce | repos/qud5pDnpzo.zip | 2a0fc2d815abab75df09a87afb520c51859a97120797bb2e3f4acdbe1f6be77b | 538,430 | 19 | {
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} | 509 | {
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} | true | 2023-06-27T23:36:29 | {
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3jBXX9Xb1iz | 2,023 | rejected | Multi-Label Knowledge Distillation | [
"Peng-Hui Yang",
"Ming-Kun Xie",
"Chen-Chen Zong",
"Lei Feng",
"Gang Niu",
"Masashi Sugiyama",
"Sheng-Jun Huang"
] | [
"~Peng-Hui_Yang1",
"~Ming-Kun_Xie1",
"~Chen-Chen_Zong1",
"~Lei_Feng1",
"~Gang_Niu1",
"~Masashi_Sugiyama1",
"~Sheng-Jun_Huang1"
] | OpenReview API | Existing knowledge distillation methods typically work by enforcing the consistency of output logits or intermediate feature maps between the teacher network and student network. Unfortunately, these methods can hardly be extended to the multi-label learning scenario. Because each instance is associated with multiple s... | Reject | null | 5 | [
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],
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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=3jBXX9Xb1iz | 2308.06453 | papers/3jBXX9Xb1iz.pdf | d4f01bc2e752d95bd47b969bb88d5f5e5384b19c5600a454b7b999267efe7e28 | 1,642,188 | openreview | https://github.com/penghui-yang/L2D | penghui-yang/L2D | ea7fa5581de3cd0dba2f4d5ba3e3aaed78837a58 | repos/3jBXX9Xb1iz.zip | 9169733a62bb098bd068e2bdc00b03d52247427651d2bb544dfd25631748b2e5 | 1,271,502 | 38 | {
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} | 1,370 | {
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} | false | 2024-04-24T05:30:44 | {
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} | {
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qLqeb9AjD2o | 2,022 | rejected | Confidence-aware Training of Smoothed Classifiers for Certified Robustness | [
"Jongheon Jeong",
"Seojin Kim",
"Jinwoo Shin"
] | [
"~Jongheon_Jeong1",
"~Seojin_Kim2",
"~Jinwoo_Shin1"
] | OpenReview API | Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to $\ell_2$-adversarial perturbations, viz., by averaging its predictions over the noise, namely via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversari... | 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=qLqeb9AjD2o | 2212.09000 | papers/qLqeb9AjD2o.pdf | c6511ef06638f442450886893c65398c8a10c07008cf210a63dfb44cb538c2eb | 837,194 | openreview | https://github.com/alinlab/smoothing-catrs | alinlab/smoothing-catrs | d4bc576e7d373d158f087ba5744af8bb48466bb7 | repos/qLqeb9AjD2o.zip | a7812fb83b825258884964045e66eed124829cfaf6b99a2f1cc6c1d62d5631bc | 7,562,839 | 18 | {
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} | 6,953 | {
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} | false | 2023-01-19T06:05:09 | {
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33TBJachvOX | 2,021 | rejected | How to compare adversarial robustness of classifiers from a global perspective | [
"Niklas Risse",
"Jan Philip Göpfert",
"Christina Göpfert"
] | [
"~Niklas_Risse1",
"jgoepfert@techfak.uni-bielefeld.de",
"~Christina_Göpfert1"
] | OpenReview API | Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a give... | Reject | null | 4 | [
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],
"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=33TBJachvOX | 2004.10882 | papers/33TBJachvOX.pdf | 1e8ddcae1a9f2cc9a0d020723415270d632d2874d0ab59100222dc68977ad9b1 | 631,544 | openreview | https://github.com/niklasrisse/how-to-compare-adversarial-robustness-of-classifiers-from-a-global-perspective | niklasrisse/how-to-compare-adversarial-robustness-of-classifiers-from-a-global-perspective | 2e03e661fbe9639913750e6044967d3ddbae4a1d | repos/33TBJachvOX.zip | 405d6c804f570ceaf1e0134bf7be236003a5990892b60326e3b44c1212ee4803 | 5,190,222 | 10 | {
".ipynb": 8,
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} | 5,052 | {
"Jupyter Notebook": 78815,
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} | false | 2020-10-02T15:35:44 | {
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YMCtQlm8Bc | 2,025 | rejected | Critical Influence of Overparameterization on Sharpness-aware Minimization | [
"Sungbin Shin",
"Dongyeop Lee",
"Maksym Andriushchenko",
"Namhoon Lee"
] | [
"~Sungbin_Shin1",
"~Dongyeop_Lee1",
"~Maksym_Andriushchenko1",
"~Namhoon_Lee1"
] | OpenReview API | Training overparameterized neural networks often yields solutions with varying generalization capabilities, even when achieving similar training losses. Recent evidence indicates a strong correlation between the sharpness of a minimum and its generalization error, leading to increased interest in optimization methods t... | Reject | 4 | [
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"id": "o1hpcMtCbp",
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],
"rating": 6,
"soundness": 3,
"presentation": 4,
"contribution": 3,
"confidence": 5,
"summary": "The authors perform experiments to \nmeasure the effect of overparameteriz... | https://openreview.net/forum?id=YMCtQlm8Bc | 2311.17539 | papers/YMCtQlm8Bc.pdf | e482abfbc585f1a91c990f9065e43a7f385a070f68a417f6326b84ab0f239203 | 1,913,746 | openreview | https://github.com/LOG-postech/SAM-overparam | LOG-postech/SAM-overparam | a96543a160555481d5db3513daf67370e9062b5e | repos/YMCtQlm8Bc.zip | af46474301cffa44ed514fbb09de6fe0eeb25bfa03baedb7ad7c2557014c3a49 | 292,216 | 9 | {
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} | 435 | {
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} | false | 2025-05-14T07:51:49 | {
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9Kgnvknvwd | 2,024 | rejected | A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization | [
"Feiyang Ye",
"Baijiong Lin",
"Xiaofeng Cao",
"Yu Zhang",
"Ivor Tsang"
] | [
"~Feiyang_Ye4",
"~Baijiong_Lin1",
"~Xiaofeng_Cao2",
"~Yu_Zhang3",
"~Ivor_Tsang1"
] | OpenReview API | In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational ... | 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=9Kgnvknvwd | 2401.09257 | papers/9Kgnvknvwd.pdf | 293c575bdb29f63088782569c988ae4dfda05b2a30a2cf3a7f3863516e93ad44 | 506,917 | openreview | https://github.com/Baijiong-Lin/FORUM | Baijiong-Lin/FORUM | 318607c503ec4ec5ce4f0d297401fc43717b026f | repos/9Kgnvknvwd.zip | f0243dac00e0edb628c5374d155a54ab6b163b3c2b7996967ee4d863f8f6f408 | 522,142 | 46 | {
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} | 511 | {
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} | false | 2024-07-04T08:05:20 | {
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mPzpPv0geS2 | 2,023 | rejected | Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models | [
"Xingyu Xie",
"Pan Zhou",
"Huan Li",
"Zhouchen Lin",
"Shuicheng YAN"
] | [
"~Xingyu_Xie1",
"~Pan_Zhou3",
"~Huan_Li1",
"~Zhouchen_Lin1",
"~Shuicheng_YAN3"
] | OpenReview API | Adaptive gradient algorithms combine the moving average idea with heavy ball acceleration to estimate accurate first- and second-order moments of the gradient for accelerating convergence. However, Nesterov acceleration which converges faster than heavy ball acceleration in theory and also in many empirical cases, is ... | Reject | null | 3 | [
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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 piece... | https://openreview.net/forum?id=mPzpPv0geS2 | 2208.06677 | papers/mPzpPv0geS2.pdf | 20fdc1ac034d4ac8f55c72e5c35542aee29bd3ddbb2a236785be3423996021c6 | 687,321 | openreview | https://github.com/sail-sg/Adan | sail-sg/Adan | 2c65beaf400775753b155da4082c3819fea689b9 | repos/mPzpPv0geS2.zip | 566e520cdcf502f66f299901533420521442849f4928e604efeb33d877c2b9cb | 1,374,021 | 40 | {
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} | 1,373 | {
"Python": 269155,
"Cuda": 21780,
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} | false | 2025-06-08T14:35:41 | {
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xWRX16GCugt | 2,022 | rejected | Sequoia: A Software Framework to Unify Continual Learning Research | [
"Fabrice Normandin",
"Oleksiy Ostapenko",
"Pau Rodriguez",
"Florian Golemo",
"Ryan Lindeborg",
"Matthew Riemer",
"Lucas Cecchi",
"Timothee LESORT",
"Khimya Khetarpal",
"David Vazquez",
"Laurent Charlin",
"Irina Rish",
"Massimo Caccia"
] | [
"~Fabrice_Normandin1",
"~Oleksiy_Ostapenko1",
"~Pau_Rodriguez2",
"~Florian_Golemo1",
"~Ryan_Lindeborg1",
"~Matthew_Riemer1",
"~Lucas_Cecchi1",
"~Timothee_LESORT1",
"~Khimya_Khetarpal1",
"~David_Vazquez1",
"~Laurent_Charlin1",
"~Irina_Rish1",
"~Massimo_Caccia1"
] | OpenReview API | The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of ass... | 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=xWRX16GCugt | 2108.01005 | papers/xWRX16GCugt.pdf | 21a8c76f54b72c288d72216822848f13692c8c98857abd6e4e9f8fe1d4d71b72 | 1,437,338 | openreview | https://github.com/lebrice/Sequoia | lebrice/Sequoia | 7e12ff8ed67fada8cf220c5c396dc26332f558c2 | repos/xWRX16GCugt.zip | 05456f055e07f93b2e86c5a0e2276450461d8d3697232848c5c22682ecd48cf9 | 926,909 | 399 | {
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} | 7,144 | {
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} | false | 2023-05-30T15:33:28 | {
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1-Mh-cWROZ | 2,021 | rejected | Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design | [
"Yue Cao",
"Payel Das",
"Pin-Yu Chen",
"Vijil Chenthamarakshan",
"Igor Melnyk",
"Yang Shen"
] | [
"~Yue_Cao4",
"~Payel_Das1",
"~Pin-Yu_Chen1",
"~Vijil_Chenthamarakshan1",
"~Igor_Melnyk1",
"~Yang_Shen4"
] | OpenReview API | Designing novel protein sequences consistent with a desired 3D structure or fold, often referred to as the inverse protein folding problem, is a central, but non-trivial, task in protein engineering. It has a wide range of applications in energy, biomedicine, and materials science. However, challenges exist due to t... | Reject | null | 4 | [
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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": "",
"s... | https://openreview.net/forum?id=1-Mh-cWROZ | 2106.13058 | papers/1-Mh-cWROZ.pdf | d83ecfc36418ba1394affebdc567665760fded62a0e85f6fc65b1ebcf908b16f | 6,948,439 | openreview | https://github.com/IBM/fold2seq | IBM/fold2seq | b9a97d81eac329b5259ad10e2a6f4fe80ade542f | repos/1-Mh-cWROZ.zip | 915d3bb6b0d7ae80a24fbac44b57eaf8e952f7d5cd410bd141857458fdbc8fec | 3,877,907 | 12 | {
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} | 5,093 | {
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} | true | 2022-05-25T06:49:33 | {
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qVyjN01x4P | 2,025 | rejected | Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift | [
"Yanru Sun",
"Zongxia Xie",
"Emadeldeen Eldele",
"Dongyue Chen",
"Qinghua Hu",
"Min Wu"
] | [
"~Yanru_Sun1",
"~Zongxia_Xie1",
"~Emadeldeen_Eldele1",
"~Dongyue_Chen3",
"~Qinghua_Hu1",
"~Min_Wu2"
] | OpenReview API | Time series forecasting, which aims to predict future values based on historical data, has garnered significant attention due to its broad range of applications. However, real-world time series often exhibit complex non-uniform distribution with varying patterns across segments, such as season, operating condition, or... | Reject | 5 | [
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"summary": "The paper innovatively addresses the diversity of time series patterns by ... | https://openreview.net/forum?id=qVyjN01x4P | 2410.09836 | papers/qVyjN01x4P.pdf | 92a2f1308f3cdb35e6581c53679d0b2b31f6940ae6780b14873147c3e5ee8ee4 | 2,627,191 | openreview | https://github.com/syrGitHub/TFPS | syrGitHub/TFPS | 83a11827e27e6617e8c8a8771f0a1dd7e10976a5 | repos/qVyjN01x4P.zip | 50228f3260690aae63dbbcdc8dc12a2ba011f5b924e2e529078267495c42086b | 428,277 | 37 | {
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sOHVDPqoUJ | 2,024 | rejected | Less is More: Selective Layer Finetuning with SubTuning | [
"Gal Kaplun",
"Andrey Gurevich",
"Tal Swisa",
"Mazor David",
"Shai Shalev-Shwartz",
"eran malach"
] | [
"~Gal_Kaplun1",
"~Andrey_Gurevich1",
"~Tal_Swisa1",
"~Mazor_David1",
"~Shai_Shalev-Shwartz1",
"~eran_malach1"
] | OpenReview API | Finetuning a pretrained model has become the standard approach for training neural networks on novel tasks, leading to rapid convergence and enhanced performance. In this work, we present a parameter-efficient finetuning method, wherein we selectively train a carefully chosen subset of layers while keeping the remainin... | Reject | 4 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "2 fair",
"presentation": "2 fair",
"contribution": "2 fair",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=sOHVDPqoUJ | 2302.06354 | papers/sOHVDPqoUJ.pdf | 2959d112a8557b8fb0f273b5a40267ddc317203704e6b6013c5decc2ae0cd045 | 2,800,232 | openreview | https://github.com/talswisa/SubTuning | talswisa/SubTuning | 68b64c24ad0b5053d4ad2b79d0c6c459c327c5a4 | repos/sOHVDPqoUJ.zip | 822a43b27e88ac40d5b78c43d7cc274e717be48903654b6bfec7556217e104bd | 522,020 | 16 | {
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} | 519 | {
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} | false | 2023-06-11T12:01:56 | {
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3vOtC1t1kF | 2,023 | rejected | Efficient Personalized Federated Learning via Sparse Model-Adaptation | [
"Daoyuan Chen",
"Liuyi Yao",
"Dawei Gao",
"Bolin Ding",
"Yaliang Li"
] | [
"~Daoyuan_Chen1",
"~Liuyi_Yao1",
"~Dawei_Gao1",
"~Bolin_Ding3",
"~Yaliang_Li1"
] | OpenReview API | Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. Due to the heterogeneity of clients' local data distribution, recent studies explore the personalized FL that learns and deploys distinct local models with the help of auxiliary global models. Howe... | Reject | null | 5 | [
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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=3vOtC1t1kF | 2305.02776 | papers/3vOtC1t1kF.pdf | 2db5442db797c005ec4a900d973d002d4f400b5716063c6ecbea64994fb61bda | 3,128,174 | openreview | https://github.com/yxdyc/pFedGate | yxdyc/pFedGate | b8cfa156acdb08207d33641a355959503ca1db78 | repos/3vOtC1t1kF.zip | a9bd83e2b07861800568ce1cd6e93a00b7bacee23f7d7d32f4653b52376c5e2a | 1,405,277 | 40 | {
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} | 1,374 | {
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} | false | 2023-05-26T07:25:36 | {
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qvUJV2-t_c | 2,022 | rejected | Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training | [
"Maximus Mutschler",
"Kevin Alexander Laube",
"Andreas Zell"
] | [
"~Maximus_Mutschler1",
"~Kevin_Alexander_Laube1",
"~Andreas_Zell1"
] | OpenReview API | A fundamental challenge in Deep Learning is to find optimal step sizes for stochastic gradient descent automatically. In traditional optimization, line searches are a commonly used method to determine step sizes. One problem in Deep Learning is that finding appropriate step sizes on the full-batch loss is unfeasibly ex... | 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=qvUJV2-t_c | 2108.13880 | papers/qvUJV2-t_c.pdf | 9f597cea7a98e0aa66702ce4e8dbe59eb0dd032694dd240317aa790df57e14c1 | 1,981,608 | openreview | https://github.com/cogsys-tuebingen/LABPAL | cogsys-tuebingen/LABPAL | f577cf976d5b88ea2dc901c2f71d30a643424a98 | repos/qvUJV2-t_c.zip | db9bba8e4e81078624f30af104f2309f8b2a59a7707809d0711fadc47dd73394 | 1,145,573 | 43 | {
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} | 7,363 | {
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} | false | 2022-09-19T14:10:35 | {
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XG1Drw7VbLJ | 2,021 | rejected | Defining Benchmarks for Continual Few-Shot Learning | [
"Antreas Antoniou",
"Massimiliano Patacchiola",
"Mateusz Ochal",
"Amos Storkey"
] | [
"~Antreas_Antoniou2",
"~Massimiliano_Patacchiola1",
"~Mateusz_Ochal1",
"~Amos_Storkey1"
] | OpenReview API | In recent years there has been substantial progress in few-shot learning, where a model is trained on a small labeled dataset related to a specific task, and in continual learning, where a model has to retain knowledge acquired on a sequence of datasets. Both of these fields are different abstractions of the same real ... | Reject | null | 4 | [
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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": "",
"pre... | https://openreview.net/forum?id=XG1Drw7VbLJ | 2004.11967 | papers/XG1Drw7VbLJ.pdf | 485efc10dcca6d91e8c84d41e64f977ac061289cbdaa2abd54b2666a74138cbc | 1,703,999 | openreview | https://github.com/AntreasAntoniou/FewShotContinualLearning | AntreasAntoniou/FewShotContinualLearning | 819b9cc26ef9d2360a040c51f17958e1b8dba8fd | repos/XG1Drw7VbLJ.zip | 2ea8a735c9dfabd031f26c81d0868989240fe2d83b3d3589450fe957cbfe12c3 | 937,410 | 448 | {
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} | 5,401 | {
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} | false | 2020-08-18T12:00:15 | {
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pjfrGVekwK | 2,025 | rejected | Variational Bayes Gaussian Splatting | [
"Toon Van de Maele",
"Ozan Catal",
"Alexander Tschantz",
"Christopher Buckley",
"Tim Verbelen"
] | [
"~Toon_Van_de_Maele1",
"~Ozan_Catal2",
"~Alexander_Tschantz2",
"~Christopher_Buckley1",
"~Tim_Verbelen1"
] | OpenReview API | Recently, 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians. The predominant optimization method for these models relies on backpropagating gradients through a differentiable rendering pipeline, which struggles with catastrophic forgetting when dealing with con... | Reject | 4 | [
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"rating": 5,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 4,
"summary": "This paper proposes Variational Bayes Gaussian Splatting (VBGS) for modeli... | https://openreview.net/forum?id=pjfrGVekwK | 2410.03592 | papers/pjfrGVekwK.pdf | 839a8697315880151fea95573a9d113d2cd1419ca17078de736876bc8abe9119 | 49,753,355 | openreview | https://github.com/VersesTech/vbgs | VersesTech/vbgs | 2ae3f4bea6ed3a5d69271c0e2a67322c06c09b9e | repos/pjfrGVekwK.zip | c0149126d32b7301091bec2b96e4035c1594e70ab93474a0de82254a4620e2c9 | 156,422 | 41 | {
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} | 438 | {
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} | false | 2025-04-11T15:56:18 | {
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BMw4Cm0gGO | 2,024 | rejected | C-MCTS: Safe Planning with Monte Carlo Tree Search | [
"Dinesh Parthasarathy",
"Georgios Kontes",
"Axel Plinge",
"Christopher Mutschler"
] | [
"~Dinesh_Parthasarathy1",
"~Georgios_Kontes1",
"~Axel_Plinge1",
"~Christopher_Mutschler1"
] | OpenReview API | The Constrained Markov Decision Process (CMDP) allows to solve safety-critical decision making tasks that are subject to constraints.
While CMDPs have been extensively studied in the Reinforcement Learning literature, little attention has been given to sampling-based planning algorithms such as MCTS for solving them. ... | Reject | 4 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "3: You are fairly confiden... | https://openreview.net/forum?id=BMw4Cm0gGO | 2305.16209 | papers/BMw4Cm0gGO.pdf | ae98759604c00dbad971d43d8dbdab354781980c377e42e492bc472337f41886 | 483,528 | openreview | https://github.com/mutschcr/C-MCTS | mutschcr/C-MCTS | f690100502cf0db548edc318b0f055ecbfcac790 | repos/BMw4Cm0gGO.zip | a92a43b669da9eaf6fac00eaa2fce613a01f8fdd1aebb66f17008ded68428b8d | 629,146 | 36 | {
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} | 537 | {
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} | false | 2023-05-25T04:37:26 | {
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MpGP-z07TmM | 2,023 | rejected | Learning Specialized Activation Functions for Physics-informed Neural Networks | [
"Honghui Wang",
"Lu Lu",
"Shiji Song",
"Gao Huang"
] | [
"~Honghui_Wang1",
"~Lu_Lu1",
"~Shiji_Song1",
"~Gao_Huang1"
] | OpenReview API | At the heart of network architectures lie the non-linear activation functions, the choice of which affects the model optimization and task performance. In computer vision and natural language processing, the Rectified Linear Unit is widely adopted across different tasks. However, there is no such default choice of acti... | 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=MpGP-z07TmM | 2308.04073 | papers/MpGP-z07TmM.pdf | 9c98e682fe8f416b08b23c73ccfd585da392fe9d1a163d93364485b87dc92019 | 1,036,724 | openreview | https://github.com/LeapLabTHU/AdaAFforPINNs | LeapLabTHU/AdaAFforPINNs | 2e87a3dd134c6bb359c8330a4d8e84f0a97201ed | repos/MpGP-z07TmM.zip | a659359d57d1659e599d17aa8e8555140585b0a83aee3968e2bf0a4028d5b61f | 1,674,830 | 8 | {
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} | 1,642 | {
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} | false | 2023-08-09T03:15:18 | {
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OBwsUF4nFye | 2,022 | rejected | Private Multi-Task Learning: Formulation and Applications to Federated Learning | [
"Shengyuan Hu",
"Steven Wu",
"Virginia Smith"
] | [
"~Shengyuan_Hu2",
"~Steven_Wu1",
"~Virginia_Smith1"
] | OpenReview API | Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relevant for privacy-sensitive applications in areas such as healthcare, finance, and IoT computing, where sensitive data from multiple, varied s... | Reject | null | 3 | [
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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=OBwsUF4nFye | 2108.12978 | papers/OBwsUF4nFye.pdf | e13fcdb2e1488db3919dd73797bbecbfff096a7ceae6f886befedf0154ffbe38 | 1,208,779 | openreview | https://github.com/s-huu/PMTL | s-huu/PMTL | 9e849ada6af2d8df92c40724d81b4b4cf2ac24b0 | repos/OBwsUF4nFye.zip | a5faf61ca8aed85f60630498ea84c5f7f0a1fe2dbce016a22a2c7e76a91eb5b7 | 7,688,047 | 18 | {
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} | 7,495 | {
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} | false | 2023-04-08T21:05:08 | {
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qn_gk5j3PJ | 2,021 | rejected | PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction | [
"Eli Simhayev",
"Gilad Katz",
"Lior Rokach"
] | [
"~Eli_Simhayev1",
"giladkz@post.bgu.ac.il",
"liorrk@post.bgu.ac.il"
] | OpenReview API | Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving their prediction of specific values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We ... | Reject | null | 4 | [
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"rating": "6: Marginally above acceptance threshold",
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"... | https://openreview.net/forum?id=qn_gk5j3PJ | 2006.05139 | papers/qn_gk5j3PJ.pdf | 09e5f450f0190b32544022760c49a533f6836077452b5b688d2998e72a161171 | 4,424,000 | openreview | https://github.com/elisim/piven | elisim/piven | ecdfc024f3e2f63b10a930039b3e6ada3c3c74d4 | repos/qn_gk5j3PJ.zip | 4174c9f5b706247f718cedf24dcbc32858f14a9aab174fbf9e1f69b7772f4cec | 5,189,515 | 19 | {
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} | false | 2023-03-12T05:51:21 | {
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7iCT2vmYAR | 2,025 | rejected | Contrastive learning of cell state dynamics in response to perturbations | [
"Soorya Pradeep",
"Alishba Imran",
"Ziwen Liu",
"Eduardo Hirata-Miyasaki",
"Taylla Milena Theodoro",
"Ivan E. Ivanov",
"Madhura Bhave",
"Sudip Khadka",
"Hunter Woosley",
"Carolina Arias",
"Shalin B. Mehta"
] | [
"~Soorya_Pradeep1",
"~Alishba_Imran1",
"~Ziwen_Liu5",
"~Eduardo_Hirata-Miyasaki1",
"~Taylla_Milena_Theodoro1",
"~Ivan_E._Ivanov1",
"~Madhura_Bhave1",
"~Sudip_Khadka1",
"~Hunter_Woosley1",
"~Carolina_Arias1",
"~Shalin_B._Mehta1"
] | OpenReview API | We introduce dynaCLR, a self-supervised framework for modeling cell and organelle dynamics via contrastive learning of representations of time-lapse datasets. Live cell imaging of cells and organelles is widely used to analyze cellular responses to perturbations. Supervised modeling of dynamic cell states encoded in 3D... | Reject | 3 | [
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"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "The authors present a self-supervised framework for leveraging contrastive... | https://openreview.net/forum?id=7iCT2vmYAR | 2410.11281 | papers/7iCT2vmYAR.pdf | a54aa992dc3b37a0890ef584d0b47c63d54628cc7d9851ca799b2dc34f21024c | 33,399,544 | openreview | https://github.com/czbiohub-sf/napari-iohub | czbiohub-sf/napari-iohub | 6a5d13f16af0b6a7865a117e9309a837743ee28c | repos/7iCT2vmYAR.zip | b9ba16113e16ed3347b8b05184ddf21dd3e603702c8b18800bf080cee9ea7930 | 36,012 | 12 | {
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} | 442 | {
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} | false | 2026-05-21T17:36:06 | {
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Gpp1dfvZYYH | 2,022 | rejected | ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training | [
"Hui-Po Wang",
"Sebastian U Stich",
"Yang He",
"Mario Fritz"
] | [
"~Hui-Po_Wang1",
"~Sebastian_U_Stich1",
"yang.he@cispa.saarland",
"~Mario_Fritz1"
] | OpenReview API | Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data. However, training is resource-intensive for edge devices, and limited network bandwidth is often the main bottleneck. Prior work often overcomes the constraints by ... | 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 piece... | https://openreview.net/forum?id=Gpp1dfvZYYH | 2110.05323 | papers/Gpp1dfvZYYH.pdf | 7e84b451cfafcd539ff5178c3ca052c1375b64b2460a244981715a8ea3659ab3 | 3,503,773 | openreview | https://github.com/hui-po-wang/ProgFed | hui-po-wang/ProgFed | 1d1eb9d83110bc31ad38447288325560fa1d9878 | repos/Gpp1dfvZYYH.zip | eeb75970647d74dc71642004cf5ffc37edccc6670f1157cdc5073530d3665b2d | 145,342 | 17 | {
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} | 7,744 | {
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} | false | 2022-10-17T15:05:45 | {
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uELnyih9gqb | 2,021 | rejected | WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION | [
"Ahmed T. Elthakeb",
"Prannoy Pilligundla",
"Tarek Elgindi",
"Fatemehsadat Mireshghallah",
"Charles-Alban Deledalle",
"Hadi Esmaeilzadeh"
] | [
"~Ahmed_T._Elthakeb1",
"~Prannoy_Pilligundla1",
"telgindi@ucsd.edu",
"~Fatemehsadat_Mireshghallah1",
"~Charles-Alban_Deledalle2",
"~Hadi_Esmaeilzadeh1"
] | OpenReview API | Deep quantization of neural networks below eight bits can lead to superlinear benefits in storage and compute efficiency. However, homogeneously quantizing all the layers to the same level does not account for the distinction of the layers and their individual properties. Heterogenous assignment of bitwidths to individ... | Reject | null | 4 | [
{
"id": "5j07Sf3fUeB",
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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=uELnyih9gqb | null | papers/uELnyih9gqb.pdf | 5a682c3fb2d6b864bfc43325e3e695ec542c92bc507c527d834559d6e44354a7 | 6,135,804 | openreview | https://github.com/waveq-reg/waveq | waveq-reg/waveq | 39bbf0dd95f5bf139197152e651acba08f5ccc68 | repos/uELnyih9gqb.zip | e62168ffadd6946d47432e0d32637d2b2c34742fd8564bc4035a23797dc70b73 | 10,720,076 | 344 | {
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".cpp": 7,
".cu": 4,
".cuh": 2,
".lua": 2,
".h": 1
} | 5,593 | {
"Python": 9078893,
"Cuda": 36414,
"C++": 15854,
"Shell": 4215,
"Lua": 4210
} | false | 2020-06-11T19:08:04 | {
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} | {
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} |
Bo5eKnJPML | 2,025 | rejected | A Reasoning-Based Approach to Cryptic Crossword Clue Solving | [
"Martin Andrews",
"Sam Witteveen"
] | [
"~Martin_Andrews1",
"~Sam_Witteveen1"
] | OpenReview API | Cryptic crossword clues are challenging language tasks for which new test sets are released daily by major newspapers on a global basis. Each cryptic clue contains both the definition of the answer to be placed in the crossword grid (in common with regular crosswords), and ‘wordplay’ that *proves* that the answer is co... | Reject | 4 | [
{
"id": "qCDw7Bhp07",
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"ICLR.cc/2025/Conference/Submission7903/Reviewer_HU2t"
],
"rating": 6,
"soundness": 3,
"presentation": 2,
"contribution": 3,
"confidence": 3,
"summary": "The paper proposes a reasoning-based system for solving cryptic crossword ... | https://openreview.net/forum?id=Bo5eKnJPML | 2506.04824 | papers/Bo5eKnJPML.pdf | 7a54ae3cbcd5c4d48606e426ede74932347f8bbb56f15cc3e28d452c5ec9f02d | 556,499 | openreview | https://github.com/mdda/cryptic-crossword-reasoning-verifier | mdda/cryptic-crossword-reasoning-verifier | 0122293fbe640f0aa4b837d37841eb0df34fec38 | repos/Bo5eKnJPML.zip | 3a2204f9bda6de0b50722aab90bc9b9711ac8344630481e9402ac1cade509954 | 418,350 | 23 | {
".py": 14,
".ipynb": 9
} | 442 | {
"Jupyter Notebook": 1208319,
"Python": 333246
} | false | 2026-01-11T18:50:18 | {
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"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/a-reasoning-based-approach-to-cryptic"
} | {
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} | |
D6aGz0Zyvn | 2,024 | rejected | Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels | [
"FAN He",
"Mingzhen He",
"Lei Shi",
"Xiaolin Huang",
"Johan Suykens"
] | [
"~FAN_He1",
"~Mingzhen_He1",
"~Lei_Shi7",
"~Xiaolin_Huang1",
"~Johan_Suykens1"
] | OpenReview API | The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function (RB... | Reject | 3 | [
{
"id": "cHrPCu2nnl",
"reviewer_signature": [
"ICLR.cc/2024/Conference/Submission2653/Reviewer_gJwQ"
],
"rating": "8: accept, good paper",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "4: You are confident in your assessment, but not ... | https://openreview.net/forum?id=D6aGz0Zyvn | 2310.05236 | papers/D6aGz0Zyvn.pdf | 1ecddbb3608915a6ebb9c6c87cc1163154f3fde7264783fb76afa2aa93518141 | 424,349 | openreview | https://github.com/hefansjtu/LABRBF_kernel | hefansjtu/LABRBF_kernel | 0ed445b3ac5bae7e0cc6bb25a5064ec028bf8436 | repos/D6aGz0Zyvn.zip | e784a7e42e8727b25f19d99595aca7b11797ca639b4a757a4d96b69c3a920416 | 553,627 | 10 | {
".py": 10
} | 556 | {
"Python": 100417
} | false | 2023-11-24T12:14:16 | {
"method": "exact_normalized_title",
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} | {
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} | |
DvMDIEFtyjV | 2,023 | rejected | CLUTR: Curriculum Learning via Unsupervised Task Representation Learning | [
"Abdus Salam Azad",
"Izzeddin Gur",
"Aleksandra Faust",
"Pieter Abbeel",
"Ion Stoica"
] | [
"~Abdus_Salam_Azad1",
"~Izzeddin_Gur1",
"~Aleksandra_Faust1",
"~Pieter_Abbeel2",
"~Ion_Stoica1"
] | OpenReview API | Reinforcement Learning (RL) algorithms are often known for sample inefficiency and difficult generalization. Recently, Unsupervised Environment Design (UED) emerged as a new paradigm for zero-shot generalization by simultaneously learning a task distribution and agent policies on the sampled tasks. This is a non-statio... | Reject | null | 3 | [
{
"id": "xZ5HWnkJMH",
"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=DvMDIEFtyjV | 2210.10243 | papers/DvMDIEFtyjV.pdf | 0aeeb5936b11bae3bd0baf887738a66d9513aed4b572c5673ea99a2bb15c8a32 | 2,658,059 | openreview | https://github.com/clutr/clutr | clutr/clutr | b461ffdfc937a231b7a5086bb520c1d3f9b88c39 | repos/DvMDIEFtyjV.zip | b439267c18b401b50d17c52ce9398cd64fef1d7d459dde1f3f2154579254a28b | 1,849,371 | 83 | {
".py": 80,
".sh": 3
} | 1,789 | {
"Python": 4654068,
"Shell": 4210
} | false | 2022-12-10T00:23:30 | {
"method": "exact_normalized_title",
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} | {
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} |
luO6l9cP6b6 | 2,022 | rejected | Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained Models | [
"Zhengxuan Wu",
"Nelson F. Liu",
"Christopher Potts"
] | [
"~Zhengxuan_Wu1",
"~Nelson_F._Liu1",
"~Christopher_Potts1"
] | OpenReview API | There is growing evidence that pretrained language models improve task-specific fine-tuning even where the task examples are radically different from those seen in training. What is the nature of this surprising cross-domain transfer? We offer a partial answer via a systematic exploration of how much transfer occurs wh... | Reject | null | 4 | [
{
"id": "tABD54QKiT",
"reviewer_signature": [
"ICLR.cc/2022/Conference/Paper859/Reviewer_vdgZ"
],
"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 o... | https://openreview.net/forum?id=luO6l9cP6b6 | 2104.08410 | papers/luO6l9cP6b6.pdf | 220871086cc7ab4dc0b183640c4e3d76dd90b87f380076f3a8a96029058fea71 | 7,142,459 | openreview | https://github.com/frankaging/limits-cross-domain-transfer | frankaging/limits-cross-domain-transfer | 318d5154f2391006709a16ab47fae3fa896c42fc | repos/luO6l9cP6b6.zip | c961790df54567a452fbdbaf6d9d6cb4d7eda53a34a63c20b1c787878a83c153 | 1,297,450 | 26 | {
".ipynb": 14,
".py": 11,
".sh": 1
} | 7,788 | {
"Jupyter Notebook": 2070243,
"Python": 227190,
"Shell": 701
} | false | 2021-11-12T06:31:52 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/identifying-the-limits-of-cross-domain"
} | {
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} |
ZvvxYyjfvZc | 2,021 | rejected | Correcting Momentum in Temporal Difference Learning | [
"Emmanuel Bengio",
"Joelle Pineau",
"Doina Precup"
] | [
"~Emmanuel_Bengio1",
"~Joelle_Pineau1",
"~Doina_Precup1"
] | OpenReview API | A common optimization tool used in deep reinforcement learning is momentum, which consists in accumulating and discounting past gradients, reapplying them at each iteration. We argue that, unlike in supervised learning, momentum in Temporal Difference (TD) learning accumulates gradients that become doubly stale: not on... | Reject | null | 4 | [
{
"id": "AXq1l9-agl6",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper1690/AnonReviewer3"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"s... | https://openreview.net/forum?id=ZvvxYyjfvZc | 2106.03955 | papers/ZvvxYyjfvZc.pdf | 4cfc3a5841809cfedf0d3e1104b1dc0fd87e27f91b738850fbe648a5039acb3a | 709,918 | openreview | https://github.com/bengioe/staleness-corrected-momentum | bengioe/staleness-corrected-momentum | ecdc5a3e49c009471c6cac8af8ebd58ed908115c | repos/ZvvxYyjfvZc.zip | 83d6a5d81ee47251e9ae49ec17beab073078da29c710bc5bf98abe77bd151831 | 5,815,991 | 12 | {
".py": 12
} | 5,680 | {
"Python": 112910
} | false | 2021-06-28T17:58:24 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/correcting-momentum-in-temporal-difference-1"
} | {
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} |
aYx7JR20sI | 2,025 | rejected | Tropical Expressivity of Neural Networks | [
"Paul Lezeau",
"Thomas Walker",
"Yueqi Cao",
"Shiv Bhatia",
"Anthea Monod"
] | [
"~Paul_Lezeau1",
"~Thomas_Walker2",
"~Yueqi_Cao1",
"~Shiv_Bhatia1",
"~Anthea_Monod1"
] | OpenReview API | We propose an algebraic geometric framework to study the expressivity of piecewise linear activation neural networks. A particular quantity of neural networks that has been actively studied is the number of linear regions, which gives a quantification of the information capacity of the architecture. To study and eval... | Reject | 4 | [
{
"id": "zIWeR57dlY",
"reviewer_signature": [
"ICLR.cc/2025/Conference/Submission12294/Reviewer_gsPs"
],
"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 2,
"summary": "This paper presents a novel approach to investigating the expressivity of... | https://openreview.net/forum?id=aYx7JR20sI | 2405.20174 | papers/aYx7JR20sI.pdf | 77dd594f146bb00e667ffdf799c9032ceeb5d1490139b3e5f29cfe9d3017d6af | 525,659 | openreview | https://github.com/Paul-Lez/tropicalnn | Paul-Lez/tropicalnn | 479ec5e96030f46fe5d0783d74ca193196512729 | repos/aYx7JR20sI.zip | ceb1903c51542bbc926e575c1322b4f3b31238713f2f846c3dff7746f43dfd85 | 61,274 | 15 | {
".jl": 14,
".sh": 1
} | 451 | {
"Julia": 66379,
"Shell": 1603
} | false | 2026-08-28T16:56:29 | {
"method": "exact_normalized_title",
"pwc_official": true,
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"pwc_url": "https://paperswithcode.com/paper/tropical-expressivity-of-neural-networks"
} | {
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} | |
YGTSLDAPqb | 2,024 | rejected | Connect Later: Improving Fine-Tuning for Robustness with Targeted Augmentations | [
"Helen Qu",
"Sang Michael Xie"
] | [
"~Helen_Qu1",
"~Sang_Michael_Xie1"
] | OpenReview API | Models trained on a labeled source domain (e.g., bright, nearby astronomical objects) often generalize poorly when deployed on an out-of-distribution (OOD) target domain (e.g., faint, distant objects). In the domain adaptation setting where unlabeled target data is available, self-supervised pretraining (e.g., masked a... | Reject | 3 | [
{
"id": "9T4OHHKswH",
"reviewer_signature": [
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],
"rating": "3: reject, not good enough",
"soundness": "1 poor",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in your assessment, but... | https://openreview.net/forum?id=YGTSLDAPqb | 2402.03325 | papers/YGTSLDAPqb.pdf | d62f4a92d56ceb897111d1b57253777107275f060253eeb3d030f22fa0ea4439 | 1,827,859 | openreview | https://github.com/helenqu/connect-later | helenqu/connect-later | 6a12eab8918ac0152fa79aa168c028e6f378a478 | repos/YGTSLDAPqb.zip | cee7e178d6e53111ce3368e94c9d1eb60da77dfdc95690c4dc139f3817f77456 | 40,610 | 23 | {
".py": 13,
".sh": 10
} | 556 | {
"Python": 111188,
"Shell": 5135
} | false | 2024-01-03T18:09:49 | {
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"pwc_url": "https://paperswithcode.com/paper/connect-later-improving-fine-tuning-for"
} | {
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IJwhRE510b | 2,023 | rejected | ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training | [
"Yue Zhao",
"Yantao Shen",
"Yuanjun Xiong",
"Shuo Yang",
"Wei Xia",
"Zhuowen Tu",
"Bernt Schiele",
"Stefano Soatto"
] | [
"~Yue_Zhao4",
"~Yantao_Shen2",
"~Yuanjun_Xiong3",
"~Shuo_Yang2",
"~Wei_Xia6",
"~Zhuowen_Tu1",
"~Bernt_Schiele1",
"~Stefano_Soatto1"
] | OpenReview API | Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy by forcing a new model to imitate the old models, or use ensembles, which multiply inference cost prohibitively. We anal... | Reject | null | 4 | [
{
"id": "phCn3MpAiO",
"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 or ... | https://openreview.net/forum?id=IJwhRE510b | 2205.06265 | papers/IJwhRE510b.pdf | 962ab09db994ac743851b1dfde9a9550e79dc50cd610caef821d32d8383aafa6 | 1,817,942 | openreview | https://github.com/amazon-science/regression-constraint-model-upgrade | amazon-science/regression-constraint-model-upgrade | fb2268c67706834632ec1427945fcf30a7218227 | repos/IJwhRE510b.zip | f96d039c962b8974340148d30689577d69def49e44d86c514613f84caa48fcbb | 1,964,441 | 45 | {
".py": 45
} | 1,809 | {
"Python": 349619
} | false | 2023-12-08T20:26:18 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/elodi-ensemble-logit-difference-inhibition"
} | {
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} |
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