id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
17788e19a5a78b04f17432163734845be109d6162adfdf0b579fcd4d610c1a95 | [
"arxiv",
"semantic_scholar"
] | Fisher Mask Nodes for Language Model Merging | Fine-tuning pre-trained models provides significant advantages in downstream performance. The ubiquitous nature of pre-trained models such as BERT and its derivatives in natural language processing has also led to a proliferation of task-specific fine-tuned models. As these models typically only perform one task well, ... | [
"Thennal D K",
"Ganesh Nathan",
"Suchithra M S"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-03-14T00:00:00 | https://arxiv.org/abs/2403.09891 | https://arxiv.org/pdf/2403.09891v3 | 2403.09891 | 10.48550/arXiv.2403.09891 | 9 | 0 | false | null | International Conference on Language Resources and Evaluation | 0.25 |
e12395dc31065654c16f4d000b45fe690fcf31589b8facdc5e5df13f6b6ce5ea | [
"arxiv",
"semantic_scholar"
] | SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes | This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate. Such cases of overgeneration put in jeopardy many NLG applications, where correctness is often mission-critical. The shared task was c... | [
"Timothee Mickus",
"Elaine Zosa",
"Raúl Vázquez",
"Teemu Vahtola",
"Jörg Tiedemann",
"Vincent Segonne",
"Alessandro Raganato",
"Marianna Apidianaki"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-03-12T00:00:00 | https://arxiv.org/abs/2403.07726 | https://arxiv.org/pdf/2403.07726v3 | 2403.07726 | 10.48550/arXiv.2403.07726 | 42 | 6 | false | null | International Workshop on Semantic Evaluation | 0.4225 |
5716178a6ce3a64b49b758de7ebe44d8aa43a51bebbb3ab5aee6710e73c37e47 | [
"arxiv",
"semantic_scholar"
] | A Segmentation Foundation Model for Diverse-type Tumors | Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks. Many publicly available medical image datasets do not have a sufficient amount of data so there are few large-scale models in medical imaging. We propose a large-scale Tumor S... | [
"Jianhao Xie",
"Ziang Zhang",
"Guibo Luo",
"Yuesheng Zhu"
] | [
"eess.IV",
"cs.CV"
] | [
"Engineering",
"Computer Science"
] | 2024-03-11T00:00:00 | https://arxiv.org/abs/2403.06396 | https://arxiv.org/pdf/2403.06396v1 | 2403.06396 | 10.48550/arXiv.2403.06396 | 0 | 0 | false | null | arXiv.org | 0 |
3c504b3ad9a012ce3011989387018499c5d0ab82bb18c6ab6d7cfcf3567bd7bd | [
"arxiv",
"semantic_scholar"
] | Training-Free Pretrained Model Merging | Recently, model merging techniques have surfaced as a solution to combine multiple single-talent models into a single multi-talent model. However, previous endeavors in this field have either necessitated additional training or fine-tuning processes, or require that the models possess the same pre-trained initializatio... | [
"Zhengqi Xu",
"Ke Yuan",
"Huiqiong Wang",
"Yong Wang",
"Mingli Song",
"Jie Song"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-03-04T00:00:00 | https://arxiv.org/abs/2403.01753 | https://arxiv.org/pdf/2403.01753v3 | 2403.01753 | 10.1109/CVPR52733.2024.00565 | 36 | 0 | true | https://github.com/zju-vipa/training_free_model_merging | Computer Vision and Pattern Recognition | 0.3921 |
97bbc26f25c0cfecc3ce52bebc8455cd8694b30312be2db9f7969c9522ce0271 | [
"arxiv",
"semantic_scholar"
] | Merging Text Transformer Models from Different Initializations | Recent work on permutation-based model merging has shown impressive low- or zero-barrier mode connectivity between models from completely different initializations. However, this line of work has not yet extended to the Transformer architecture, despite its dominant popularity in the language domain. Therefore, in this... | [
"Neha Verma",
"Maha Elbayad"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-03-01T00:00:00 | https://arxiv.org/abs/2403.00986 | https://arxiv.org/pdf/2403.00986v3 | 2403.00986 | 10.48550/arXiv.2403.00986 | 15 | 1 | false | null | null | 0.301 |
72f48530cbcd9beeb969f2ab0c42661455ff0d298a65e8591be8d7aa8def508e | [
"arxiv",
"semantic_scholar"
] | Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic | Aligned language models face a significant limitation as their fine-tuning often results in compromised safety. To tackle this, we propose a simple method RESTA that performs LLM safety realignment. RESTA stands for REstoring Safety through Task Arithmetic. At its core, it involves a simple arithmetic addition of a saf... | [
"Rishabh Bhardwaj",
"Do Duc Anh",
"Soujanya Poria"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-02-19T00:00:00 | https://arxiv.org/abs/2402.11746 | https://arxiv.org/pdf/2402.11746v1 | 2402.11746 | 10.48550/arXiv.2402.11746 | 106 | 14 | true | https://github.com/declare-lab/resta | Annual Meeting of the Association for Computational Linguistics | 0.588 |
a1e3c7ec9b0520560ab9bd60709056519850bb92b6d4a63f65984a68f51b4726 | [
"arxiv",
"semantic_scholar"
] | WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More | Large Language Models (LLMs) face significant deployment challenges due to their substantial memory requirements and the computational demands of auto-regressive text generation process. This paper addresses these challenges by focusing on the quantization of LLMs, a technique that reduces memory consumption by convert... | [
"Yuxuan Yue",
"Zhihang Yuan",
"Haojie Duanmu",
"Sifan Zhou",
"Jianlong Wu",
"Liqiang Nie"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-02-19T00:00:00 | https://arxiv.org/abs/2402.12065 | https://arxiv.org/pdf/2402.12065v2 | 2402.12065 | 10.48550/arXiv.2402.12065 | 85 | 3 | false | null | arXiv.org | 0.4836 |
c2e884c616d99c0b45de7f42f49d7c491805537ba6d607e924412c2438ec1a06 | [
"arxiv",
"semantic_scholar"
] | Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents | Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents. To resolve this problem, previous w... | [
"Renxi Wang",
"Haonan Li",
"Xudong Han",
"Yixuan Zhang",
"Timothy Baldwin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-18T00:00:00 | https://arxiv.org/abs/2402.11651 | https://arxiv.org/pdf/2402.11651v2 | 2402.11651 | 10.48550/arXiv.2402.11651 | 46 | 4 | false | null | arXiv.org | 0.418 |
6dfa928b137534c5e038f8544820da357cf14b81f471f03c71ceeed777ab191f | [
"arxiv",
"semantic_scholar"
] | Pelican Soup Framework: A Theoretical Framework for Language Model Capabilities | In this work, we propose a simple theoretical framework, Pelican Soup, aiming to better understand how pretraining allows LLMs to (1) generalize to unseen instructions and (2) perform in-context learning, even when the verbalizers are irrelevant to the task. To this end, in our framework, we introduce the notion of "kn... | [
"Ting-Rui Chiang",
"Dani Yogatama"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-02-16T00:00:00 | https://arxiv.org/abs/2402.10424 | https://arxiv.org/pdf/2402.10424v2 | 2402.10424 | 10.18653/v1/2026.findings-eacl.23 | 3 | 0 | false | null | Conference of the European Chapter of the Association for Computational Linguistics | 0.1505 |
7c68eda12ca260f0e2fe8896f5fb5567c8b2f57a8ec118ca9a8d5a19f564b530 | [
"arxiv",
"semantic_scholar"
] | Representation Surgery for Multi-Task Model Merging | Multi-task learning (MTL) compresses the information from multiple tasks into a unified backbone to improve computational efficiency and generalization. Recent work directly merges multiple independently trained models to perform MTL instead of collecting their raw data for joint training, greatly expanding the applica... | [
"Enneng Yang",
"Li Shen",
"Zhenyi Wang",
"Guibing Guo",
"Xiaojun Chen",
"Xingwei Wang",
"Dacheng Tao"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2024-02-05T00:00:00 | https://arxiv.org/abs/2402.02705 | https://arxiv.org/pdf/2402.02705v2 | 2402.02705 | 10.48550/arXiv.2402.02705 | 104 | 11 | false | null | International Conference on Machine Learning | 0.5396 |
7638c58ec905a473a4ea4112c7d4ef10a0947a2817b4a515efc3b12aadefa56d | [
"arxiv",
"semantic_scholar"
] | Merging Multi-Task Models via Weight-Ensembling Mixture of Experts | Merging various task-specific Transformer-based models trained on different tasks into a single unified model can execute all the tasks concurrently. Previous methods, exemplified by task arithmetic, have been proven to be both effective and scalable. Existing methods have primarily focused on seeking a static optimal ... | [
"Anke Tang",
"Li Shen",
"Yong Luo",
"Nan Yin",
"Lefei Zhang",
"Dacheng Tao"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2024-02-01T00:00:00 | https://arxiv.org/abs/2402.00433 | https://arxiv.org/pdf/2402.00433v2 | 2402.00433 | 10.48550/arXiv.2402.00433 | 99 | 13 | true | https://github.com/tanganke/weight-ensembling_MoE | International Conference on Machine Learning | 0.5731 |
e8bd04765f835107f832036521f2a71da6fdc098b00987eb1f729ad8b42039a1 | [
"arxiv",
"semantic_scholar"
] | RADIN: Souping on a Budget | Model Soups, extending Stochastic Weights Averaging (SWA), combine models fine-tuned with different hyperparameters. Yet, their adoption is hindered by computational challenges due to subset selection issues. In this paper, we propose to speed up model soups by approximating soups performance using averaged ensemble lo... | [
"Thibaut Menes",
"Olivier Risser-Maroix"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2024-01-31T00:00:00 | https://arxiv.org/abs/2401.17790 | https://arxiv.org/pdf/2401.17790v1 | 2401.17790 | 10.48550/arXiv.2401.17790 | 1 | 0 | false | null | arXiv.org | 0.0753 |
9cbc2ae410bc3e48828c92d7ca8ef5165650e95657c3000c5c4bcd4ffdc7ca50 | [
"arxiv",
"semantic_scholar"
] | Active Inference as a Model of Agency | Is there a canonical way to think of agency beyond reward maximisation? In this paper, we show that any type of behaviour complying with physically sound assumptions about how macroscopic biological agents interact with the world canonically integrates exploration and exploitation in the sense of minimising risk and am... | [
"Lancelot Da Costa",
"Samuel Tenka",
"Dominic Zhao",
"Noor Sajid"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2024-01-23T00:00:00 | https://arxiv.org/abs/2401.12917 | https://arxiv.org/pdf/2401.12917v1 | 2401.12917 | 10.48550/arXiv.2401.12917 | 16 | 2 | false | null | arXiv.org | 0.3076 |
12843b8c954813f221897042e5d835db3ef3aa5d4ffd6b6611b5d29eda95802d | [
"arxiv",
"semantic_scholar"
] | CLIP Model for Images to Textual Prompts Based on Top-k Neighbors | Text-to-image synthesis, a subfield of multimodal generation, has gained significant attention in recent years. We propose a cost-effective approach for image-to-prompt generation that leverages generative models to generate textual prompts without the need for large amounts of annotated data. We divide our method into... | [
"Xin Zhang",
"Xin Zhang",
"YeMing Cai",
"Tianzhi Jia"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2024-01-18T00:00:00 | https://arxiv.org/abs/2401.09763 | https://arxiv.org/pdf/2401.09763v1 | 2401.09763 | 10.1109/EIECS59936.2023.10435489 | 2 | 0 | false | null | null | 0.1193 |
18fcca95943e51d2702adcd30900841dc0e750f405eeefdf754550b624b5021e | [
"arxiv",
"semantic_scholar"
] | Erasing Undesirable Influence in Diffusion Models | Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content. Although various techniques have been proposed to mitigate unwanted influences in diffusion models while preserving overall performance, achieving a balance b... | [
"Jing Wu",
"Trung Le",
"Munawar Hayat",
"Mehrtash Harandi"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-01-11T00:00:00 | https://arxiv.org/abs/2401.05779 | https://arxiv.org/pdf/2401.05779v4 | 2401.05779 | 10.1109/CVPR52734.2025.02632 | 41 | 6 | false | null | Computer Vision and Pattern Recognition | 0.4225 |
24418c7947f9ccffd4ab0a6c799f77800098fb070f1e012311313c976d9396a4 | [
"arxiv",
"semantic_scholar"
] | Merging Vision Transformers from Different Tasks and Domains | This work targets to merge various Vision Transformers (ViTs) trained on different tasks (i.e., datasets with different object categories) or domains (i.e., datasets with the same categories but different environments) into one unified model, yielding still good performance on each task or domain. Previous model mergin... | [
"Peng Ye",
"Chenyu Huang",
"Mingzhu Shen",
"Tao Chen",
"Yongqi Huang",
"Yuning Zhang",
"Wanli Ouyang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2023-12-25T00:00:00 | https://arxiv.org/abs/2312.16240 | https://arxiv.org/pdf/2312.16240v1 | 2312.16240 | 10.48550/arXiv.2312.16240 | 20 | 1 | false | null | arXiv.org | 0.3306 |
90bb504f9c9dabcbee76afa6327ad677fa167d68c23e0a099c905bcc21c45b9a | [
"arxiv",
"semantic_scholar"
] | Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks | The rapid development of AI systems has been greatly influenced by the emergence of foundation models. A common approach for targeted problems involves fine-tuning these pre-trained foundation models for specific target tasks, resulting in a rapid spread of models fine-tuned across a diverse array of tasks. This work f... | [
"MohammadReza Davari",
"Eugene Belilovsky"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06795 | https://arxiv.org/pdf/2312.06795v2 | 2312.06795 | 10.48550/arXiv.2312.06795 | 130 | 11 | true | null | European Conference on Computer Vision | 0.5396 |
8fc405edf05abccb4ed4920eba8116a92f73e9bd758c5deb8a34d5ef250687e2 | [
"arxiv",
"semantic_scholar"
] | Merging by Matching Models in Task Parameter Subspaces | Model merging aims to cheaply combine individual task-specific models into a single multitask model. In this work, we view past merging methods as leveraging different notions of a ''task parameter subspace'' in which models are matched before being merged. We connect the task parameter subspace of a given model to its... | [
"Derek Tam",
"Mohit Bansal",
"Colin Raffel"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04339 | https://arxiv.org/pdf/2312.04339v2 | 2312.04339 | null | 30 | 3 | true | https://github.com/r-three/mats | null | 0.3728 |
6ac5bc2b17c92fb1f9cae5098e3b4430b28e582c22fe804209d3ae5815ac5d53 | [
"arxiv",
"semantic_scholar"
] | Advances in the equivariant minimal model program and their applications in complex and arithmetic dynamics | This note reports some advances in the Equivariant Minimal Model Program (EMMP) for non-isomorphic surjective endomorphisms and their applications in complex and arithmetic dynamics. | [
"Sheng Meng",
"De-Qi Zhang"
] | [
"math.AG",
"math.DS",
"math.NT"
] | [
"Mathematics"
] | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.16369 | https://arxiv.org/pdf/2311.16369v1 | 2311.16369 | 10.1007/978-3-032-04048-0_4 | 8 | 4 | false | null | DeMarco, L., Jonsson, M. (eds) Algebraic, Complex, and Arithmetic Dynamics. Simons Symposia. Springer, Cham. yr 2026, pages 99-123 | 0.3495 |
df1e874c85efbc5727e0229ca9923523e0a29352361e4ce75edabba001f445bd | [
"arxiv",
"semantic_scholar"
] | Model Theory of Ultrafinitism II: Deconstructing the Term Model (First Draft) | This paper presents a novel possible worlds semantics, designed to elucidate the underpinnings of ultrafinitism. By constructing a careful modification of the well-known Kripke models for inuitionistic logic, we seek to extend our comprehension of the ultra-finite mindset. As it turns out, the passage from standard con... | [
"Mirco A. Mannucci"
] | [
"math.LO",
"cs.LO"
] | [
"Mathematics",
"Computer Science"
] | 2023-11-26T00:00:00 | https://arxiv.org/abs/2311.17931 | https://arxiv.org/pdf/2311.17931v1 | 2311.17931 | 10.48550/arXiv.2311.17931 | 0 | 0 | false | null | arXiv.org | 0 |
48dd35cd0752349bfb25bba9eb2cf109a8349769b764f9b7d68b2c611b76b1d7 | [
"arxiv",
"semantic_scholar"
] | Orca 2: Teaching Small Language Models How to Reason | Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs' reasoning abilities. Research on training small LMs has often... | [
"Arindam Mitra",
"Luciano Del Corro",
"Shweti Mahajan",
"Andres Codas",
"Clarisse Simoes",
"Sahaj Agarwal",
"Xuxi Chen",
"Anastasia Razdaibiedina",
"Erik Jones",
"Kriti Aggarwal",
"Hamid Palangi",
"Guoqing Zheng",
"Corby Rosset",
"Hamed Khanpour",
"Ahmed Awadallah"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2023-11-18T00:00:00 | https://arxiv.org/abs/2311.11045 | https://arxiv.org/pdf/2311.11045v2 | 2311.11045 | 10.48550/arXiv.2311.11045 | 203 | 17 | false | null | arXiv.org | 0.6276 |
c73feb5738386fecd98077c0ba0bd3eb71297f63cb73d62cc32c5e53ce00b84b | [
"arxiv",
"semantic_scholar"
] | PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion | Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a c... | [
"Yiduo Guo",
"Zekai Zhang",
"Yaobo Liang",
"Dongyan Zhao",
"Nan Duan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-11-03T00:00:00 | https://arxiv.org/abs/2311.01767 | https://arxiv.org/pdf/2311.01767v2 | 2311.01767 | 10.48550/arXiv.2311.01767 | 30 | 4 | true | https://github.com/gydpku/PPTC} | Annual Meeting of the Association for Computational Linguistics | 0.3728 |
f40bb48f8ff9401d0a44c442ec42131e4a5ceccda687c8cd30c7e15db8481d37 | [
"arxiv",
"semantic_scholar"
] | Model Merging by Uncertainty-Based Gradient Matching | Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. ... | [
"Nico Daheim",
"Thomas Möllenhoff",
"Edoardo Maria Ponti",
"Iryna Gurevych",
"Mohammad Emtiyaz Khan"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2023-10-19T00:00:00 | https://arxiv.org/abs/2310.12808 | https://arxiv.org/pdf/2310.12808v2 | 2310.12808 | 10.48550/arXiv.2310.12808 | 90 | 7 | true | https://github.com/UKPLab/iclr2024-model-merging | International Conference on Learning Representations | 0.4898 |
3e2a7ecf50bf40e41986eb2987bc7740bfc76f6ff42dfa4d9975106a6b584a49 | [
"arxiv",
"semantic_scholar"
] | AdaMerging: Adaptive Model Merging for Multi-Task Learning | Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously. A recent development known as task arithmetic has revealed that several models, each fine-tuned for distinct tasks, can be directly merged into a single model to execute MTL without necessitating a retraining process using the in... | [
"Enneng Yang",
"Zhenyi Wang",
"Li Shen",
"Shiwei Liu",
"Guibing Guo",
"Xingwei Wang",
"Dacheng Tao"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2023-10-04T00:00:00 | https://arxiv.org/abs/2310.02575 | https://arxiv.org/pdf/2310.02575v2 | 2310.02575 | 10.48550/arXiv.2310.02575 | 251 | 49 | false | null | International Conference on Learning Representations | 0.8495 |
f9f5af4b05e69d84a46f2720a830267b7626e1098965fa1e851a5d3b98fa04d9 | [
"arxiv",
"semantic_scholar"
] | Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance | Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem. Leveraging these local optima to improve model performance remains a challenging task. Given the inherent complexity of neural networks, t... | [
"Hao Chen",
"Yusen Wu",
"Phuong Nguyen",
"Chao Liu",
"Yelena Yesha"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-09-21T00:00:00 | https://arxiv.org/abs/2309.12259 | https://arxiv.org/pdf/2309.12259v1 | 2309.12259 | 10.48550/arXiv.2309.12259 | 0 | 0 | false | null | arXiv.org | 0 |
1228aed58d0055d19b8c959ff70dd01706e62c39d913f12f1e0e3648a1b174a6 | [
"arxiv",
"semantic_scholar"
] | Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models | We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming langua... | [
"Neha Sengupta",
"Sunil Kumar Sahu",
"Bokang Jia",
"Satheesh Katipomu",
"Haonan Li",
"Fajri Koto",
"William Marshall",
"Gurpreet Gosal",
"Cynthia Liu",
"Zhiming Chen",
"Osama Mohammed Afzal",
"Samta Kamboj",
"Onkar Pandit",
"Rahul Pal",
"Lalit Pradhan",
"Zain Muhammad Mujahid",
"Mass... | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-08-30T00:00:00 | https://arxiv.org/abs/2308.16149 | https://arxiv.org/pdf/2308.16149v2 | 2308.16149 | 10.48550/arXiv.2308.16149 | 81 | 8 | false | null | arXiv.org | 0.4785 |
0c4d4c25e58d3df50baf1e9e97d2ef83020f666d639d233208e3cc873bc3875e | [
"arxiv",
"semantic_scholar"
] | Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup | The standard recipe applied in transfer learning is to finetune a pretrained model on the task-specific dataset with different hyperparameter settings and pick the model with the highest accuracy on the validation dataset. Unfortunately, this leads to models which do not perform well under distribution shifts, e.g. whe... | [
"Hannes Fassold"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-08-28T00:00:00 | https://arxiv.org/abs/2309.08610 | https://arxiv.org/pdf/2309.08610v1 | 2309.08610 | 10.48550/arXiv.2309.08610 | 2 | 0 | false | null | arXiv.org | 0.1193 |
359a9f43b73be1ae8b68d02d70a11b2c929954c81d4809c08be6a4d172098efe | [
"arxiv",
"semantic_scholar"
] | Testing different Log Bases For Vector Model Weighting Technique | Information retrieval systems retrieves relevant documents based on a query submitted by the user. The documents are initially indexed and the words in the documents are assigned weights using a weighting technique called TFIDF which is the product of Term Frequency (TF) and Inverse Document Frequency (IDF). TF represe... | [
"Kamel Assaf"
] | [
"cs.IR",
"cs.AI"
] | [
"Computer Science"
] | 2023-07-12T00:00:00 | https://arxiv.org/abs/2307.06213 | https://arxiv.org/pdf/2307.06213v1 | 2307.06213 | 10.5121/ijnlc.2023.12301 | 0 | 0 | false | null | International Journal on Natural Language Computing | 0 |
449368457b7e4ac759e65dcfcfc211dea651c7098e34835201caf6a17dddfc58 | [
"arxiv",
"semantic_scholar"
] | TBGC: Task-level Backbone-Oriented Gradient Clip for Multi-Task Foundation Model Learning | The AllInOne training paradigm squeezes a wide range of tasks into a unified model in a multi-task learning manner. However, optimization in multi-task learning is more challenge than single-task learning, as the gradient norm from different tasks may vary greatly, making the backbone overly biased towards one specific... | [
"Zelun Zhang",
"Xue Pan"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2023-07-07T00:00:00 | https://arxiv.org/abs/2307.03465 | https://arxiv.org/pdf/2307.03465v1 | 2307.03465 | 10.48550/arXiv.2307.03465 | 0 | 0 | false | null | arXiv.org | 0 |
8c1b1df24db248ecde782eddfc6095117f94be642a0d0cf3da7df321d92613d2 | [
"arxiv",
"semantic_scholar"
] | A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task | In recent years large model trained on huge amount of cross-modality data, which is usually be termed as foundation model, achieves conspicuous accomplishment in many fields, such as image recognition and generation. Though achieving great success in their original application case, it is still unclear whether those fo... | [
"Shiqi Yang",
"Atsushi Hashimoto",
"Yoshitaka Ushiku"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2023-07-06T00:00:00 | https://arxiv.org/abs/2307.02862 | https://arxiv.org/pdf/2307.02862v2 | 2307.02862 | 10.48550/arXiv.2307.02862 | 1 | 0 | false | null | arXiv.org | 0.0753 |
5b041de7260623b1492c0988a7d3868b43ecb49875106769cd84c61c6de33980 | [
"arxiv",
"semantic_scholar"
] | Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging | Neural networks can be significantly compressed by pruning, yielding sparse models with reduced storage and computational demands while preserving predictive performance. Model soups (Wortsman et al., 2022) enhance generalization and out-of-distribution (OOD) performance by averaging the parameters of multiple models i... | [
"Max Zimmer",
"Christoph Spiegel",
"Sebastian Pokutta"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2023-06-29T00:00:00 | https://arxiv.org/abs/2306.16788 | https://arxiv.org/pdf/2306.16788v3 | 2306.16788 | 10.48550/arXiv.2306.16788 | 22 | 1 | false | null | International Conference on Learning Representations | 0.3404 |
fad81fd31133cd7516f98da301cae63fcab4654ed40279c46d410dbcc32115d1 | [
"arxiv",
"semantic_scholar"
] | Low-Rank Prune-And-Factorize for Language Model Compression | The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy. Matrix factorization, a well-established technique from matrix theory, has been utilized to reduce the number of parameters in PLM. However, it fails to retain satisfactory performance under moderate to high compres... | [
"Siyu Ren",
"Kenny Q. Zhu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-06-25T00:00:00 | https://arxiv.org/abs/2306.14152 | https://arxiv.org/pdf/2306.14152v1 | 2306.14152 | 10.48550/arXiv.2306.14152 | 19 | 1 | false | null | International Conference on Language Resources and Evaluation | 0.3253 |
2e4c5da564e29bd0fec7fba2ee0db41b2dafbdb3b61df2f0ff07f0f4688fc024 | [
"arxiv",
"semantic_scholar"
] | Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards | Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to suboptimal results; ... | [
"Alexandre Ramé",
"Guillaume Couairon",
"Mustafa Shukor",
"Corentin Dancette",
"Jean-Baptiste Gaya",
"Laure Soulier",
"Matthieu Cord"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2023-06-07T00:00:00 | https://arxiv.org/abs/2306.04488 | https://arxiv.org/pdf/2306.04488v2 | 2306.04488 | 10.48550/arXiv.2306.04488 | 252 | 35 | false | null | Neural Information Processing Systems | 0.7782 |
8d45ab4b36ccb812b56bc774b27276e415acde39ec1875f3e0aa013eda05b006 | [
"arxiv",
"semantic_scholar"
] | TIES-Merging: Resolving Interference When Merging Models | Transfer learning - i.e., further fine-tuning a pre-trained model on a downstream task - can confer significant advantages, including improved downstream performance, faster convergence, and better sample efficiency. These advantages have led to a proliferation of task-specific fine-tuned models, which typically can on... | [
"Prateek Yadav",
"Derek Tam",
"Leshem Choshen",
"Colin Raffel",
"Mohit Bansal"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01708 | https://arxiv.org/pdf/2306.01708v2 | 2306.01708 | 10.52202/075280-0310 | 763 | 207 | true | https://github.com/prateeky2806/ties-merging | Neural Information Processing Systems | 1 |
3997689b467b637c16b76e9dd3e22e3bd84348a74367fef05e99a2e5c0e70069 | [
"arxiv",
"semantic_scholar"
] | Latent Diffusion Model Based Foley Sound Generation System For DCASE Challenge 2023 Task 7 | Foley sound presents the background sound for multimedia content and the generation of Foley sound involves computationally modelling sound effects with specialized techniques. In this work, we proposed a system for DCASE 2023 challenge task 7: Foley Sound Synthesis. The proposed system is based on AudioLDM, which is a... | [
"Yi Yuan",
"Haohe Liu",
"Xubo Liu",
"Xiyuan Kang",
"Mark D. Plumbley",
"Wenwu Wang"
] | [
"cs.SD",
"cs.MM",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2023-05-25T00:00:00 | https://arxiv.org/abs/2305.15905 | https://arxiv.org/pdf/2305.15905v3 | 2305.15905 | 10.48550/arXiv.2305.15905 | 11 | 2 | false | null | arXiv.org | 0.2698 |
7420aa9ccdb2f04bfd32a53a745bc0cc773b11a92c9e4374e0fd95f3d8f5219e | [
"arxiv",
"semantic_scholar"
] | Adapting Language Models to Compress Contexts | Transformer-based language models (LMs) are powerful and widely-applicable tools, but their usefulness is constrained by a finite context window and the expensive computational cost of processing long text documents. We propose to adapt pre-trained LMs into AutoCompressors. These language models are capable of compress... | [
"Alexis Chevalier",
"Alexander Wettig",
"Anirudh Ajith",
"Danqi Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.14788 | https://arxiv.org/pdf/2305.14788v2 | 2305.14788 | 10.48550/arXiv.2305.14788 | 340 | 30 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.7457 |
8955556fa2222a75dbf62343a5a1dee7cfe69be86edbe6ebfc960feb9af8fe95 | [
"arxiv",
"semantic_scholar"
] | Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models | Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space: By adding the fine-tuned weights of different tasks, the model's performance can be improved on these tasks, while negating them leads to task forgetting. Yet, our understanding of the eff... | [
"Guillermo Ortiz-Jimenez",
"Alessandro Favero",
"Pascal Frossard"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.12827 | https://arxiv.org/pdf/2305.12827v3 | 2305.12827 | 10.48550/arXiv.2305.12827 | 221 | 16 | false | null | Neural Information Processing Systems | 0.6152 |
4dbeedd6e5b4a89c200ca4afad9b337ed039a4ee6d92cc4f67d1850032837765 | [
"arxiv",
"semantic_scholar"
] | MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning | With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task models on edge devices is inefficient regarding the required computatio... | [
"Md Adnan Arefeen",
"Zhouyu Li",
"Md Yusuf Sarwar Uddin",
"Anupam Das"
] | [
"cs.CV",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2023-05-13T00:00:00 | https://arxiv.org/abs/2305.07815 | https://arxiv.org/pdf/2305.07815v1 | 2305.07815 | 10.1145/3576842.3582372 | 0 | 0 | false | null | International Conference on Internet-of-Things Design and Implementation | 0 |
b2c950f56cf7e45ba34a0e041e7ac60db327e0fabd6cdb051d141c7ed740c04e | [
"arxiv",
"semantic_scholar"
] | HACK: Learning a Parametric Head and Neck Model for High-fidelity Animation | Significant advancements have been made in developing parametric models for digital humans, with various approaches concentrating on parts such as the human body, hand, or face. Nevertheless, connectors such as the neck have been overlooked in these models, with rich anatomical priors often unutilized. In this paper, w... | [
"Longwen Zhang",
"Zijun Zhao",
"Xinzhou Cong",
"Qixuan Zhang",
"Shuqi Gu",
"Yuchong Gao",
"Rui Zheng",
"Wei Yang",
"Lan Xu",
"Jingyi Yu"
] | [
"cs.GR"
] | [
"Computer Science"
] | 2023-05-08T00:00:00 | https://arxiv.org/abs/2305.04469 | https://arxiv.org/pdf/2305.04469v1 | 2305.04469 | 10.1145/3592093 | 17 | 0 | true | https://github.com/ZoneLikeWonderland/HACK-Model | ACM Transactions on Graphics | 0.3138 |
947af38b39c5d4f1064239c6549c7181cc287e1c3a68090e3fd5c01b095e0e60 | [
"arxiv",
"semantic_scholar"
] | ZipIt! Merging Models from Different Tasks without Training | Typical deep visual recognition models are capable of performing the one task they were trained on. In this paper, we tackle the extremely difficult problem of combining distinct models with different initializations, each solving a separate task, into one multi-task model without any additional training. Prior work in... | [
"George Stoica",
"Daniel Bolya",
"Jakob Bjorner",
"Pratik Ramesh",
"Taylor Hearn",
"Judy Hoffman"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2023-05-04T00:00:00 | https://arxiv.org/abs/2305.03053 | https://arxiv.org/pdf/2305.03053v3 | 2305.03053 | 10.48550/arXiv.2305.03053 | 195 | 19 | false | null | International Conference on Learning Representations | 0.6505 |
088e45e71740d3b664934a2f93796c41ba02689c97245e1f9e0541c2ef39e815 | [
"arxiv",
"semantic_scholar"
] | Predict NAS Multi-Task by Stacking Ensemble Models using GP-NAS | Accurately predicting the performance of architecture with small sample training is an important but not easy task. How to analysis and train dataset to overcome overfitting is the core problem we should deal with. Meanwhile if there is the mult-task problem, we should also think about if we can take advantage of their... | [
"Ke Zhang"
] | [
"cs.LG",
"cs.CV",
"stat.AP",
"stat.CO"
] | [
"Computer Science",
"Mathematics"
] | 2023-05-02T00:00:00 | https://arxiv.org/abs/2305.01667 | https://arxiv.org/pdf/2305.01667v1 | 2305.01667 | 10.48550/arXiv.2305.01667 | 0 | 0 | false | null | arXiv.org | 0 |
a0a3232939aed41524a12267dac1b66f5754adc771eff0e4b0a4840c328471d6 | [
"arxiv",
"semantic_scholar"
] | An Empirical Study of Multimodal Model Merging | Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven successful in previous studies, where the models are trained on similar tasks and with the same initialization. In this paper, we expand on this co... | [
"Yi-Lin Sung",
"Linjie Li",
"Kevin Lin",
"Zhe Gan",
"Mohit Bansal",
"Lijuan Wang"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2023-04-28T00:00:00 | https://arxiv.org/abs/2304.14933 | https://arxiv.org/pdf/2304.14933v2 | 2304.14933 | 10.48550/arXiv.2304.14933 | 57 | 1 | true | https://github.com/ylsung/vl-merging | Conference on Empirical Methods in Natural Language Processing | 0.4409 |
a8abd931ab3d779ef8a4fcba309820cfe86a4e1a9412aa7b71918bf7aa1a1156 | [
"arxiv",
"semantic_scholar"
] | HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge | Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks. Nevertheless, LLMs have not yet performed optimally in biomedical domain tasks due to the need for medical expertise in the responses. In response to this chall... | [
"Haochun Wang",
"Chi Liu",
"Nuwa Xi",
"Zewen Qiang",
"Sendong Zhao",
"Bing Qin",
"Ting Liu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-04-14T00:00:00 | https://arxiv.org/abs/2304.06975 | https://arxiv.org/pdf/2304.06975v1 | 2304.06975 | 10.48550/arXiv.2304.06975 | 290 | 25 | true | https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese | arXiv.org | 0.7075 |
05043c4892f1a9f7493c0a4f01f2d58b449fa9b8727ac4c843f3043dbe31e1ea | [
"arxiv",
"semantic_scholar"
] | Adjust factor with volatility model using MAXFLAT low-pass filter and construct portfolio in China A share market | In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model a... | [
"Ke Zhang"
] | [
"q-fin.RM",
"q-fin.ST"
] | [
"Economics"
] | 2023-03-29T00:00:00 | https://arxiv.org/abs/2304.04676 | https://arxiv.org/pdf/2304.04676v2 | 2304.04676 | null | 1 | 0 | false | null | null | 0.0753 |
36a257c08aa10dfefe4939d4cdd4a01b23eea6ad4581143a61ec24473191c7bd | [
"arxiv",
"semantic_scholar"
] | Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies | Recent work has shown the promise of creating generalist, transformer-based, models for language, vision, and sequential decision-making problems. To create such models, we generally require centralized training objectives, data, and compute. It is of interest if we can more flexibly create generalist policies by mergi... | [
"Daniel Lawson",
"Ahmed H. Qureshi"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2023-03-14T00:00:00 | https://arxiv.org/abs/2303.07551 | https://arxiv.org/pdf/2303.07551v3 | 2303.07551 | 10.1109/ICRA57147.2024.10610919 | 15 | 2 | true | https://github.com/daniellawson9999/merging-decision-transformers | IEEE International Conference on Robotics and Automation | 0.301 |
0731e37079725049d04fb30f9c08fbdf15dc29a128da801b1852ecdab8654b8e | [
"arxiv",
"semantic_scholar"
] | Toward Defining a Domain Complexity Measure Across Domains | Artificial Intelligence (AI) systems planned for deployment in real-world applications frequently are researched and developed in closed simulation environments where all variables are controlled and known to the simulator or labeled benchmark datasets are used. Transition from these simulators, testbeds, and benchmark... | [
"Katarina Doctor",
"Christine Task",
"Eric Kildebeck",
"Mayank Kejriwal",
"Lawrence Holder",
"Russell Leong"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2023-03-07T00:00:00 | https://arxiv.org/abs/2303.04141 | https://arxiv.org/pdf/2303.04141v1 | 2303.04141 | 10.48550/arXiv.2303.04141 | 10 | 2 | false | null | arXiv.org | 0.2603 |
7642ac038b3bc81b3b6c1b1ea1d0d0856133425cc041d6bdcd7b62946d2cc230 | [
"arxiv",
"semantic_scholar"
] | Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News | This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup.'' Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and mult... | [
"Yuta Koreeda",
"Ken-ichi Yokote",
"Hiroaki Ozaki",
"Atsuki Yamaguchi",
"Masaya Tsunokake",
"Yasuhiro Sogawa"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2023-03-03T00:00:00 | https://arxiv.org/abs/2303.01794 | https://arxiv.org/pdf/2303.01794v2 | 2303.01794 | 10.18653/v1/2023.semeval-1.237 | 3 | 0 | false | null | International Workshop on Semantic Evaluation | 0.1505 |
8726ddc7761ff438489e794d0f1ddfa171f7211f5fc0e1a28712e0ae622a8acf | [
"arxiv",
"semantic_scholar"
] | Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts | Adversarial training is widely used to make classifiers robust to a specific threat or adversary, such as $\ell_p$-norm bounded perturbations of a given $p$-norm. However, existing methods for training classifiers robust to multiple threats require knowledge of all attacks during training and remain vulnerable to unsee... | [
"Francesco Croce",
"Sylvestre-Alvise Rebuffi",
"Evan Shelhamer",
"Sven Gowal"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2023-02-20T00:00:00 | https://arxiv.org/abs/2302.10164 | https://arxiv.org/pdf/2302.10164v1 | 2302.10164 | 10.1109/CVPR52729.2023.01185 | 23 | 2 | false | null | Computer Vision and Pattern Recognition | 0.3451 |
60a8eb09320628f095ae2ce7a720d9ef1231ca8665bb3576e769ab6bb1c6261f | [
"arxiv",
"semantic_scholar"
] | Zilber's notion of logically perfect structure: Universal Covers | We sketch recent interactions between model theory and a roughly 150-year old study of analytic functions involving complex analysis, algebraic topology, and number theory, centered in canonicity of universal covers. Towards this goal we discuss in a systematic and unified way several examples indicating the main ideas... | [
"John T. Baldwin",
"Andrés Villaveces"
] | [
"math.LO"
] | [
"Mathematics"
] | 2023-02-09T00:00:00 | https://arxiv.org/abs/2302.04650 | https://arxiv.org/pdf/2302.04650v3 | 2302.04650 | 10.2140/mt.2024.3.647 | 7 | 1 | false | null | Model Th. 3 (2024) 647-683 | 0.2258 |
16776bc8de2ca19bcb0882e78081e34a989d813fc5e8325d2e6ec73b1b976eda | [
"arxiv",
"semantic_scholar"
] | Backward Compatibility During Data Updates by Weight Interpolation | Backward compatibility of model predictions is a desired property when updating a machine learning driven application. It allows to seamlessly improve the underlying model without introducing regression bugs. In classification tasks these bugs occur in the form of negative flips. This means an instance that was correct... | [
"Raphael Schumann",
"Elman Mansimov",
"Yi-An Lai",
"Nikolaos Pappas",
"Xibin Gao",
"Yi Zhang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2023-01-25T00:00:00 | https://arxiv.org/abs/2301.10546 | https://arxiv.org/pdf/2301.10546v1 | 2301.10546 | 10.48550/arXiv.2301.10546 | 7 | 1 | false | null | Conference of the European Chapter of the Association for Computational Linguistics | 0.2258 |
5612f4b4ebf284d6b2ac1bbcde9390a4ecf018a47ca2bb4b11dc879d552f0dd2 | [
"arxiv",
"semantic_scholar"
] | Dataless Knowledge Fusion by Merging Weights of Language Models | Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training data is not, due to data privacy or intellectual property concerns. This creates a barrier to fusing knowledge across individual models to y... | [
"Xisen Jin",
"Xiang Ren",
"Daniel Preotiuc-Pietro",
"Pengxiang Cheng"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2022-12-19T00:00:00 | https://arxiv.org/abs/2212.09849 | https://arxiv.org/pdf/2212.09849v6 | 2212.09849 | 10.48550/arXiv.2212.09849 | 388 | 69 | true | https://github.com/bloomberg/dataless-model-merging | International Conference on Learning Representations | 0.9225 |
385817bdb1c35abf723aac3315f39e9ebe64c685ee7fb704299e9f4693357e6b | [
"arxiv",
"semantic_scholar"
] | Editing Models with Task Arithmetic | Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems. In this work, we propose a new paradigm for steering the behavior of neural networks, centered around \texti... | [
"Gabriel Ilharco",
"Marco Tulio Ribeiro",
"Mitchell Wortsman",
"Suchin Gururangan",
"Ludwig Schmidt",
"Hannaneh Hajishirzi",
"Ali Farhadi"
] | [
"cs.LG",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2022-12-08T00:00:00 | https://arxiv.org/abs/2212.04089 | https://arxiv.org/pdf/2212.04089v3 | 2212.04089 | 10.48550/arXiv.2212.04089 | 1,041 | 279 | false | null | International Conference on Learning Representations | 1 |
b00efef6a6fc88cfc94f229aca06d4c66cf649f4e3e3fa544d8a51d2a1e85044 | [
"arxiv",
"semantic_scholar"
] | Task-Driven Hybrid Model Reduction for Dexterous Manipulation | In contact-rich tasks, like dexterous manipulation, the hybrid nature of making and breaking contact creates challenges for model representation and control. For example, choosing and sequencing contact locations for in-hand manipulation, where there are thousands of potential hybrid modes, is not generally tractable. ... | [
"Wanxin Jin",
"Michael Posa"
] | [
"cs.RO",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2022-11-30T00:00:00 | https://arxiv.org/abs/2211.16657 | https://arxiv.org/pdf/2211.16657v2 | 2211.16657 | 10.1109/TRO.2024.3359531 | 21 | 2 | true | https://github.com/wanxinjin/Task-Driven-Hybrid-Reduction | IEEE Transactions on robotics | 0.3356 |
664a87f57af3cd5d180d4ee81431f7a09d03fae9adf2b4f1c1c7b86e66ce3aef | [
"arxiv",
"semantic_scholar"
] | Modelling COVID-19-III: endemic spread in India | A disease in a given population is termed endemic when it exhibits a steady prevalence. We address the pertinent question as to what extent COVID-19 has turned endemic in India. There are several existing models for studying endemic behaviour, such as the extensions of the traditional temporal SIR model or the spatio-t... | [
"Madhuchhanda Bhattacharjee",
"Arup Bose"
] | [
"stat.AP"
] | [
"Mathematics"
] | 2022-11-11T00:00:00 | https://arxiv.org/abs/2211.06215 | https://arxiv.org/pdf/2211.06215v1 | 2211.06215 | null | 0 | 0 | false | null | null | 0 |
8248004dd3f126e04246c8c4e128c2453f2940ee29c04b0224804fb896241438 | [
"arxiv",
"semantic_scholar"
] | Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning | Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts. In addition, subsequent fine-tuning can considerably improve performance on a selected downstream task. However, through naive fine-tuning,... | [
"Zafir Stojanovski",
"Karsten Roth",
"Zeynep Akata"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2022-11-06T00:00:00 | https://arxiv.org/abs/2211.03186 | https://arxiv.org/pdf/2211.03186v1 | 2211.03186 | 10.48550/arXiv.2211.03186 | 19 | 3 | false | null | arXiv.org | 0.3253 |
2e7aefcc61141b7d9ec6d2b493c3fb26f50645a1a99342c1abac51eff834fa28 | [
"arxiv",
"semantic_scholar"
] | Where to start? Analyzing the potential value of intermediate models | Previous studies observed that finetuned models may be better base models than the vanilla pretrained model. Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset. Here, we perform a systematic analysis of this intertraining scheme, ... | [
"Leshem Choshen",
"Elad Venezian",
"Shachar Don-Yehia",
"Noam Slonim",
"Yoav Katz"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-10-31T00:00:00 | https://arxiv.org/abs/2211.00107 | https://arxiv.org/pdf/2211.00107v3 | 2211.00107 | 10.48550/arXiv.2211.00107 | 30 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3728 |
dc86c27cffd75f86396f7e7c59ab719309678c0a3cf9c4c049fe04002b0ae906 | [
"arxiv",
"semantic_scholar"
] | Higher internal covers | We define and study a higher-dimensional version of model theoretic internality, and relate it to higher-dimensional definable groupoids in the base theory. | [
"Moshe Kamensky"
] | [
"math.LO",
"math.CT"
] | [
"Mathematics"
] | 2022-10-06T00:00:00 | https://arxiv.org/abs/2210.02699 | https://arxiv.org/pdf/2210.02699v2 | 2210.02699 | 10.2140/mt.2023.2.449 | 0 | 0 | false | null | Model Th. 2 (2023) 449-479 | 0 |
b0b121dda62409ac21c90c941532e2c08311f9e0f0f701d2140e50254f2ec97f | [
"arxiv",
"semantic_scholar"
] | Fractional Gagliardo-Nirenberg interpolation inequality and bounded mean oscillation | We prove Gagliardo-Nirenberg interpolation inequalities estimating the Sobolev semi-norm in terms of the bounded mean oscillation semi-norm and a Sobolev semi-norm, with some of the Sobolev semi-norms having fractional order. | [
"Jean Van Schaftingen"
] | [
"math.CA"
] | [
"Mathematics"
] | 2022-08-31T00:00:00 | https://arxiv.org/abs/2208.14691 | https://arxiv.org/pdf/2208.14691v3 | 2208.14691 | 10.5802/crmath.463 | 7 | 1 | false | null | Comptes rendus. Mathematique | 0.2258 |
209a14ff922a2c84ba3ac9661679d1c0312bedc75f0f6c286cc050a8d4ac810b | [
"arxiv",
"semantic_scholar"
] | Patching open-vocabulary models by interpolating weights | Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance i... | [
"Gabriel Ilharco",
"Mitchell Wortsman",
"Samir Yitzhak Gadre",
"Shuran Song",
"Hannaneh Hajishirzi",
"Simon Kornblith",
"Ali Farhadi",
"Ludwig Schmidt"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-08-10T00:00:00 | https://arxiv.org/abs/2208.05592 | https://arxiv.org/pdf/2208.05592v2 | 2208.05592 | 10.48550/arXiv.2208.05592 | 215 | 38 | false | null | Neural Information Processing Systems | 0.7955 |
ff130d892375d56be05f79909fc7e00952cfd2a5c0922e2b053f35c4bf061a8b | [
"arxiv",
"semantic_scholar"
] | Understanding Weight Similarity of Neural Networks via Chain Normalization Rule and Hypothesis-Training-Testing | We present a weight similarity measure method that can quantify the weight similarity of non-convex neural networks. To understand the weight similarity of different trained models, we propose to extract the feature representation from the weights of neural networks. We first normalize the weights of neural networks by... | [
"Guangcong Wang",
"Guangrun Wang",
"Wenqi Liang",
"Jianhuang Lai"
] | [
"cs.LG",
"cs.CV",
"math.ST",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-08-08T00:00:00 | https://arxiv.org/abs/2208.04369 | https://arxiv.org/pdf/2208.04369v1 | 2208.04369 | 10.48550/arXiv.2208.04369 | 5 | 0 | false | null | arXiv.org | 0.1945 |
8a79b13f689f78e400c93031b3d7e6719043e21a2972269548090db20c3b28c6 | [
"arxiv",
"semantic_scholar"
] | On the Usability of Transformers-based models for a French Question-Answering task | For many tasks, state-of-the-art results have been achieved with Transformer-based architectures, resulting in a paradigmatic shift in practices from the use of task-specific architectures to the fine-tuning of pre-trained language models. The ongoing trend consists in training models with an ever-increasing amount of ... | [
"Oralie Cattan",
"Christophe Servan",
"Sophie Rosset"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2022-07-19T00:00:00 | https://arxiv.org/abs/2207.09150 | https://arxiv.org/pdf/2207.09150v1 | 2207.09150 | 10.26615/978-954-452-072-4_029 | 15 | 1 | false | null | Recent Advances in Natural Language Processing | 0.301 |
fa6a6521d44fe13d6307d030e24a9a86f209823f2a589f2ed59826e6ab4229af | [
"arxiv",
"semantic_scholar"
] | A SIQRB delayed model for cholera and optimal control treatment | We improve a recent mathematical model for cholera by adding a time delay that represents the time between the instant at which an individual becomes infected and the instant at which he begins to have symptoms of cholera disease. We prove that the delayed cholera model is biologically meaningful and analyze the local ... | [
"Ana P. Lemos-Paiao",
"Helmut Maurer",
"Cristiana J. Silva",
"Delfim F. M. Torres"
] | [
"math.OC",
"q-bio.PE"
] | [
"Mathematics",
"Biology"
] | 2022-06-25T00:00:00 | https://arxiv.org/abs/2206.12688 | https://arxiv.org/pdf/2206.12688v1 | 2206.12688 | 10.1051/mmnp/2022027 | 14 | 1 | false | null | Mathematical Modelling of Natural Phenomena | 0.294 |
fb3be48833eea97af148d4982b502c8b89c5f1d155f2254c30816a1e37c07a7e | [
"arxiv",
"semantic_scholar"
] | Classical Aspects of a Distributional 3+1 Foam Model | A 3+1 spacetime, with a shift vector that is the unique fundamental solution to the linearized wave operator, is introduced to model an interpretation of Wheeler's layman's analogy of the Quantum foam. To understand the distributional aspects of this model is the guaranteed existence of a sequence of compactly supporte... | [
"Claes Cramer"
] | [
"gr-qc",
"math-ph",
"quant-ph"
] | [
"Physics",
"Mathematics"
] | 2022-06-21T00:00:00 | https://arxiv.org/abs/2206.10417 | https://arxiv.org/pdf/2206.10417v11 | 2206.10417 | null | 0 | 0 | false | null | null | 0 |
e8dc77fa2f412f46222a98d4cad80854faf2f6c7cc6941e94f3e8cf7537af00c | [
"arxiv",
"semantic_scholar"
] | UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language | Patronizing and condescending language (PCL) is everywhere, but rarely is the focus on its use by media towards vulnerable communities. Accurately detecting PCL of this form is a difficult task due to limited labeled data and how subtle it can be. In this paper, we describe our system for detecting such language which ... | [
"David Koleczek",
"Alex Scarlatos",
"Siddha Karakare",
"Preshma Linet Pereira"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2022-04-18T00:00:00 | https://arxiv.org/abs/2204.08304 | https://arxiv.org/pdf/2204.08304v1 | 2204.08304 | 10.48550/arXiv.2204.08304 | 1 | 0 | false | null | International Workshop on Semantic Evaluation | 0.0753 |
c17294dfc20d954ae895fc434ed6cdff85067bdba21dd67ff80f9351543a79ae | [
"arxiv",
"semantic_scholar"
] | HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity | This paper describes our system designed for SemEval-2022 Task 8: Multilingual News Article Similarity. We proposed a linguistics-inspired model trained with a few task-specific strategies. The main techniques of our system are: 1) data augmentation, 2) multi-label loss, 3) adapted R-Drop, 4) samples reconstruction wit... | [
"Zihang Xu",
"Ziqing Yang",
"Yiming Cui",
"Zhigang Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2022-04-11T00:00:00 | https://arxiv.org/abs/2204.04844 | https://arxiv.org/pdf/2204.04844v1 | 2204.04844 | 10.48550/arXiv.2204.04844 | 8 | 2 | false | null | International Workshop on Semantic Evaluation | 0.2386 |
723e6d1888f5f93fdaf9d4bf96f8e528734ea47fb4d5755c357480de95f6abc7 | [
"arxiv",
"semantic_scholar"
] | Long-Tailed Recognition via Weight Balancing | In the real open world, data tends to follow long-tailed class distributions, motivating the well-studied long-tailed recognition (LTR) problem. Naive training produces models that are biased toward common classes in terms of higher accuracy. The key to addressing LTR is to balance various aspects including data distri... | [
"Shaden Alshammari",
"Yu-Xiong Wang",
"Deva Ramanan",
"Shu Kong"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2022-03-27T00:00:00 | https://arxiv.org/abs/2203.14197 | https://arxiv.org/pdf/2203.14197v1 | 2203.14197 | 10.1109/CVPR52688.2022.00677 | 196 | 28 | true | https://github.com/ShadeAlsha/LTR-weight-balancing | Computer Vision and Pattern Recognition | 0.7312 |
ee63389abbb5e6afd52ce1bcc42f2ccb1eeac58164774d6395e3c69feced84ce | [
"arxiv",
"semantic_scholar"
] | Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time | The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large... | [
"Mitchell Wortsman",
"Gabriel Ilharco",
"Samir Yitzhak Gadre",
"Rebecca Roelofs",
"Raphael Gontijo-Lopes",
"Ari S. Morcos",
"Hongseok Namkoong",
"Ali Farhadi",
"Yair Carmon",
"Simon Kornblith",
"Ludwig Schmidt"
] | [
"cs.LG",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2022-03-10T00:00:00 | https://arxiv.org/abs/2203.05482 | https://arxiv.org/pdf/2203.05482v3 | 2203.05482 | 10.48550/arXiv.2203.05482 | 1,565 | 192 | true | https://github.com/mlfoundations/model-soups | International Conference on Machine Learning | 1 |
e8f60c07e4b269ece44a7009a2e7e90aaffd5c4668b2c6b687324b511ef284fe | [
"arxiv",
"semantic_scholar"
] | Triple Motion Estimation and Frame Interpolation based on Adaptive Threshold for Frame Rate Up-Conversion | In this paper, we propose a novel motion-compensated frame rate up-conversion (MC-FRUC) algorithm. The proposed algorithm creates interpolated frames by first estimating motion vectors using unilateral (jointing forward and backward) and bilateral motion estimation. Then motion vectors are combined based on adaptive th... | [
"Hanieh Naderi",
"Mohammad Rahmati"
] | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.MM"
] | [
"Engineering",
"Computer Science"
] | 2022-03-05T00:00:00 | https://arxiv.org/abs/2203.03621 | https://arxiv.org/pdf/2203.03621v1 | 2203.03621 | 10.48550/arXiv.2203.03621 | 0 | 0 | false | null | arXiv.org | 0 |
4776b7a754e2e630469baf90d21410631a79bfc25ece756c1cad1e4081149ee8 | [
"arxiv",
"semantic_scholar"
] | Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning | In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks. However, deep learning in resource-limited domains still faces multiple challenges inc... | [
"Pin-Yu Chen"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-02-22T00:00:00 | https://arxiv.org/abs/2202.10629 | https://arxiv.org/pdf/2202.10629v4 | 2202.10629 | 10.1609/aaai.v38i20.30267 | 87 | 4 | true | https://github.com/IBM/model-reprogramming | AAAI Conference on Artificial Intelligence | 0.4861 |
b7c17ad48ab990be9f90f51ebb8835b89e8b79dcda5a32e0120b067c43e87103 | [
"arxiv",
"semantic_scholar"
] | FILM: Frame Interpolation for Large Motion | We present a frame interpolation algorithm that synthesizes multiple intermediate frames from two input images with large in-between motion. Recent methods use multiple networks to estimate optical flow or depth and a separate network dedicated to frame synthesis. This is often complex and requires scarce optical flow ... | [
"Fitsum Reda",
"Janne Kontkanen",
"Eric Tabellion",
"Deqing Sun",
"Caroline Pantofaru",
"Brian Curless"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2022-02-10T00:00:00 | https://arxiv.org/abs/2202.04901 | https://arxiv.org/pdf/2202.04901v4 | 2202.04901 | 10.1007/978-3-031-20071-7_15 | 238 | 47 | true | https://github.com/google-research/frame-interpolation | European Conference on Computer Vision | 0.8406 |
ffee7bd98a6878025ec36e2fb397100cbcd6a9f86f689b16b752ad52a5147de7 | [
"arxiv",
"semantic_scholar"
] | Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding | Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding. Scene understanding enables better semantic segmentation and object-to-object interaction detection. In the medical domain, a robust surgical scene understanding model allows the automation of... | [
"Lalithkumar Seenivasan",
"Sai Mitheran",
"Mobarakol Islam",
"Hongliang Ren"
] | [
"eess.IV",
"cs.RO"
] | [
"Engineering",
"Computer Science"
] | 2022-01-28T00:00:00 | https://arxiv.org/abs/2201.11957 | https://arxiv.org/pdf/2201.11957v1 | 2201.11957 | 10.1109/LRA.2022.3146544 | 46 | 0 | true | https://github.com/lalithjets/Global-reasoned-multi-task-model | IEEE Robotics and Automation Letters | 0.418 |
aa0ffa9a8de37e85c387720b4480db74a94c91e5ffee730533ebcd63fa765b61 | [
"arxiv",
"semantic_scholar"
] | Arithmetic Monodromy Groups of Dynamical Belyi maps | We consider a large family of dynamical Belyi maps of arbitrary degree and study the arithmetic monodromy groups attached to the iterates of such maps. Building on the results of Bouw-Ejder-Karemaker on the geometric monodromy groups of these maps, we show that the quotient of the arithmetic monodromy group by the geom... | [
"Ozlem Ejder"
] | [
"math.NT"
] | [
"Mathematics"
] | 2022-01-22T00:00:00 | https://arxiv.org/abs/2201.09005 | https://arxiv.org/pdf/2201.09005v1 | 2201.09005 | null | 5 | 0 | false | null | null | 0.1945 |
f94c08bf8846279809568f12e503970be7f6415d35dbb03100e3a47f56a06e39 | [
"arxiv",
"semantic_scholar"
] | Arithmetic geometric model for the renormalisation of irrationally indifferent attractors | In this paper we build a geometric model for the renormalisation of irrationally indifferent fixed points. The geometric model incorporates the fine arithmetic properties of the rotation number at the fixed point. Using this model for the renormalisation, we build a topological model for the dynamics of a holomorphic m... | [
"Davoud Cheraghi"
] | [
"math.DS",
"math.FA"
] | [
"Mathematics",
"Physics"
] | 2021-12-29T00:00:00 | https://arxiv.org/abs/2112.14557 | https://arxiv.org/pdf/2112.14557v4 | 2112.14557 | 10.1088/1361-6544/ad0279 | 2 | 1 | false | null | Nonlinearity | 0.1505 |
4cbd23bb997c6caeb87eb7f799117f9ac5662a2a3d8c76dd6682534e2da4ed00 | [
"arxiv",
"semantic_scholar"
] | Modeling the debonding process of osseointegrated implants due to coupled adhesion and friction | Cementless implants have become widely used for total hip replacement surgery. The long-term stability of these implants is achieved by bone growing around and into the porous surface of the implant, a process called osseointegration. However, debonding of the bone-implant interface can still occur due to aseptic impla... | [
"Katharina Immel",
"Vu-Hieu Nguyen",
"Guillaume Haiat",
"Roger A. Sauer"
] | [
"physics.med-ph",
"cs.CE"
] | [
"Computer Science",
"Physics",
"Medicine"
] | 2021-12-13T00:00:00 | https://arxiv.org/abs/2112.06793 | https://arxiv.org/pdf/2112.06793v2 | 2112.06793 | 10.1007/s10237-022-01637-7 | 7 | 0 | false | null | Biomechanics and Modeling in Mechanobiology | 0.2258 |
f07c63b47d071dd7944334bf04e7189603d597538a3a9eb11805e9f6bf410087 | [
"arxiv",
"semantic_scholar"
] | Merging Models with Fisher-Weighted Averaging | Averaging the parameters of models that have the same architecture and initialization can provide a means of combining their respective capabilities. In this paper, we take the perspective that this "merging" operation can be seen as choosing parameters that approximately maximize the joint likelihood of the posteriors... | [
"Michael Matena",
"Colin Raffel"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-11-18T00:00:00 | https://arxiv.org/abs/2111.09832 | https://arxiv.org/pdf/2111.09832v2 | 2111.09832 | 10.52202/068431-1287 | 635 | 84 | false | null | Neural Information Processing Systems | 0.9647 |
5378acbe20da0b8fdbdbc06899f488c9ffece815ea92f3a1302c0cff00935ef8 | [
"arxiv",
"semantic_scholar"
] | Worst case expansions of complete theories | Given a complete theory $T$ and a subset $Y \subseteq X^k$, we precisely determine the {\em worst case complexity}, with respect to further monadic expansions, of an expansion $(M,Y)$ by $Y$ of a model $M$ of $T$ with universe $X$. In particular, although by definition monadically stable/NIP theories are robust under a... | [
"Samuel Braunfeld",
"Michael C. Laskowski"
] | [
"math.LO"
] | [
"Mathematics"
] | 2021-07-22T00:00:00 | https://arxiv.org/abs/2107.10920 | https://arxiv.org/pdf/2107.10920v2 | 2107.10920 | 10.2140/mt.2022.1.15 | 5 | 1 | false | null | Model Th. 1 (2022) 15-30 | 0.1945 |
470fd8abb2e9af7fe04279721da06a545a1cfe1a19cf483531d0d4d21ecbede1 | [
"arxiv",
"semantic_scholar"
] | Self-training with noisy student model and semi-supervised loss function for dcase 2021 challenge task 4 | This report proposes a polyphonic sound event detection (SED) method for the DCASE 2021 Challenge Task 4. The proposed SED model consists of two stages: a mean-teacher model for providing target labels regarding weakly labeled or unlabeled data and a self-training-based noisy student model for predicting strong labels ... | [
"Nam Kyun Kim",
"Hong Kook Kim"
] | [
"cs.SD",
"cs.LG",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2021-07-06T00:00:00 | https://arxiv.org/abs/2107.02569 | https://arxiv.org/pdf/2107.02569v1 | 2107.02569 | null | 14 | 1 | false | null | arXiv.org | 0.294 |
02b0495b4938a34a44958de4b5cf6d7f058f0fbdb74917c25a086958dc7673ca | [
"arxiv",
"semantic_scholar"
] | Interpolation and Model Checking for Nonlinear Arithmetic | We present a new model-based interpolation procedure for satisfiability modulo theories (SMT). The procedure uses a new mode of interaction with the SMT solver that we call solving modulo a model. This either extends a given partial model into a full model for a set of assertions or returns an explanation (a model inte... | [
"Dejan Jovanović",
"Bruno Dutertre"
] | [
"cs.LO",
"cs.PL",
"cs.SC"
] | [
"Computer Science"
] | 2021-06-08T00:00:00 | https://arxiv.org/abs/2106.04340 | https://arxiv.org/pdf/2106.04340v1 | 2106.04340 | 10.1007/978-3-030-81688-9_13 | 4 | 1 | false | null | International Conference on Computer Aided Verification | 0.1747 |
36de292de3329dc39d77c23462908988c91a54df4b1b1d57bf3109a6fac7f19f | [
"arxiv",
"semantic_scholar"
] | Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning | The task of classifying emotions within a musical track has received widespread attention within the Music Information Retrieval (MIR) community. Music emotion recognition has traditionally relied on the use of acoustic features, verbal features, and metadata-based filtering. The role of musical prosody remains under-e... | [
"Nicholas Farris",
"Brian Model",
"Richard Savery",
"Gil Weinberg"
] | [
"cs.SD",
"cs.LG",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2021-06-04T00:00:00 | https://arxiv.org/abs/2106.02556 | https://arxiv.org/pdf/2106.02556v2 | 2106.02556 | null | 1 | 0 | false | null | arXiv.org | 0.0753 |
b8321bd11df614dc10182956d8c23e9d0889bc68415c7e40987e515027383f2a | [
"arxiv",
"semantic_scholar"
] | Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers | There is growing interest in data-driven weather prediction (DDWP), for example using convolutional neural networks such as U-NETs that are trained on data from models or reanalysis. Here, we propose 3 components to integrate with commonly used DDWP models in order to improve their physical consistency and forecast acc... | [
"Ashesh Chattopadhyay",
"Mustafa Mustafa",
"Pedram Hassanzadeh",
"Eviatar Bach",
"Karthik Kashinath"
] | [
"physics.ao-ph",
"cs.AI",
"cs.LG",
"physics.comp-ph"
] | [
"Computer Science",
"Physics"
] | 2021-03-16T00:00:00 | https://arxiv.org/abs/2103.09360 | https://arxiv.org/pdf/2103.09360v1 | 2103.09360 | 10.5194/GMD-2021-71 | 42 | 3 | false | null | arXiv.org | 0.4084 |
695ab4feac12ddcff9312b75eca1cbbb9e27fbb960762249f2afe8577c651ecf | [
"arxiv",
"semantic_scholar"
] | Analysis of Interpolation based Image In-painting Approaches | Interpolation and internal painting are one of the basic approaches in image internal painting, which is used to eliminate undesirable parts that occur in digital images or to enhance faulty parts. This study was designed to compare the interpolation algorithms used in image in-painting in the literature. Errors and no... | [
"Mustafa Zor",
"Erkan Bostanci",
"Mehmet Serdar Guzel",
"Erinc Karatas"
] | [
"cs.CV",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2021-02-12T00:00:00 | https://arxiv.org/abs/2102.06564 | https://arxiv.org/pdf/2102.06564v1 | 2102.06564 | 10.1201/9781003221333-8 | 1 | 0 | false | null | null | 0.0753 |
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