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2206.13102 | Modeling content creator incentives on algorithm-curated platforms | https://openreview.net/forum?id=l6CpxixmUg | https://openreview.net/forum?id=l6CpxixmUg | Jiri Hron,Karl Krauth,Michael Jordan,Niki Kilbertus,Sarah Dean | ICLR 2023,Top 5% | Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user ... | https://openreview.net/pdf/12c4dfbbd1516c36a132fe1e8e1205b88da0540b.pdf | cs.GT cs.CY cs.IR cs.LG stat.ML | 2023-07-07T00:00:00 | modeling content creator incentives on algorithm-curated platforms | 1 | 35 | 1 |
2210.04398 | Scaling Up Probabilistic Circuits by Latent Variable Distillation | https://openreview.net/forum?id=067CGykiZTS | https://openreview.net/forum?id=067CGykiZTS | Anji Liu,Honghua Zhang,Guy Van den Broeck | ICLR 2023,Top 5% | Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries (e.g., marginal probabilities). One key challenge is to scale PCs to model large and high-dimensional real-world datasets: we observe that as the number of paramete... | https://openreview.net/pdf/03a72f57ccbfd43e91ba786ca0f782f4065669e5.pdf | cs.LG cs.AI | 2024-12-12T00:00:00 | scaling up probabilistic circuits by latent variable distillation | 3 | 24 | 3 |
2211.14810 | A Kernel Perspective of Skip Connections in Convolutional Networks | https://openreview.net/forum?id=6H_uOfcwiVh | https://openreview.net/forum?id=6H_uOfcwiVh | Daniel Barzilai,Amnon Geifman,Meirav Galun,Ronen Basri | ICLR 2023,Top 5% | Over-parameterized residual networks (ResNets) are amongst the most successful convolutional neural architectures for image processing. Here we study their properties through their Gaussian Process and Neural Tangent kernels. We derive explicit formulas for these kernels, analyze their spectra, and provide bounds on th... | https://openreview.net/pdf/d02ce0a1fbf33b0f5c0f942e925ba67c6bcfaab5.pdf | cs.LG | 2023-03-02T00:00:00 | a kernel perspective of skip connections in convolutional networks | 4 | 11 | 4 |
2210.12152 | WikiWhy: Answering and Explaining Cause-and-Effect Questions | https://openreview.net/forum?id=vaxnu-Utr4l | https://openreview.net/forum?id=vaxnu-Utr4l | Matthew Ho,Aditya Sharma,Justin Chang,Michael Saxon,Sharon Levy,Yujie Lu,William Yang Wang | ICLR 2023,Top 5% | As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (QA) benchmarks that attempt to assess reasoning are often limited by a narrow scope of covered situations and subject matters. We introduce ... | https://openreview.net/pdf/dd230e9938db73b0fff7ee629cb682af034688fc.pdf | cs.CL cs.AI | 2022-12-01T00:00:00 | wikiwhy: answering and explaining cause-and-effect questions | 5 | 16 | 5 |
2209.04836 | Git Re-Basin: Merging Models modulo Permutation Symmetries | https://openreview.net/forum?id=CQsmMYmlP5T | https://openreview.net/forum?id=CQsmMYmlP5T | Samuel Ainsworth,Jonathan Hayase,Siddhartha Srinivasa | ICLR 2023,Top 5% | The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-hard, simple algorithms -- often variants of stochastic gradient descent -- exhibit surprising effectiveness in fitting large neural network... | https://openreview.net/pdf/b212b96bd3f13e202965581f6173495898534b76.pdf | cs.LG cs.AI | 2023-03-03T00:00:00 | git re-basin: merging models modulo permutation symmetries | 6 | 313 | 6 |
2210.04157 | The Role of Coverage in Online Reinforcement Learning | https://openreview.net/forum?id=LQIjzPdDt3q | https://openreview.net/forum?id=LQIjzPdDt3q | Tengyang Xie,Dylan J Foster,Yu Bai,Nan Jiang,Sham M. Kakade | ICLR 2023,Top 5% | Coverage conditions---which assert that the data logging distribution adequately covers the state space---play a fundamental role in determining the sample complexity of offline reinforcement learning. While such conditions might seem irrelevant to online reinforcement learning at first glance, we establish a new conne... | https://openreview.net/pdf/a2c365918c8b9f3e5b7cd871606f05d90118525a.pdf | cs.LG cs.AI math.OC stat.ML | 2022-10-11T00:00:00 | the role of coverage in online reinforcement learning | 7 | 57 | 7 |
2212.04717 | On the Sensitivity of Reward Inference to Misspecified Human Models | https://openreview.net/forum?id=hJqGbUpDGV | https://openreview.net/forum?id=hJqGbUpDGV | Joey Hong,Kush Bhatia,Anca Dragan | ICLR 2023,Top 5% | Inferring reward functions from human behavior is at the center of value alignment – aligning AI objectives with what we, humans, actually want. But doing so relies on models of how humans behave given their objectives. After decades of research in cognitive science, neuroscience, and behavioral economics, obtaining ac... | https://openreview.net/pdf/787489763506d1437ac7b05b15f89ea0beb8c3b1.pdf | cs.LG cs.AI | 2023-10-31T00:00:00 | on the sensitivity of reward inference to misspecified human models | 14 | 24 | 14 |
2209.14988 | DreamFusion: Text-to-3D using 2D Diffusion | https://openreview.net/forum?id=FjNys5c7VyY | https://openreview.net/forum?id=FjNys5c7VyY | Ben Poole,Ajay Jain,Jonathan T. Barron,Ben Mildenhall | ICLR 2023,Top 5% | Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D or multiview data and efficient architectures for denoising 3D data, neither of which currently exist. In ... | https://openreview.net/pdf/fc5d88df1a06d30ae79fb23e87030f0fb2c8bd76.pdf | cs.CV cs.LG stat.ML | 2022-09-30T00:00:00 | dreamfusion: text-to-3d using 2d diffusion | 19 | 2,265 | 19 |
2210.03629 | ReAct: Synergizing Reasoning and Acting in Language Models | https://openreview.net/forum?id=WE_vluYUL-X | https://openreview.net/forum?id=WE_vluYUL-X | Shunyu Yao,Jeffrey Zhao,Dian Yu,Nan Du,Izhak Shafran,Karthik R Narasimhan,Yuan Cao | ICLR 2023,Top 5% | While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we ... | https://openreview.net/pdf/bc117919562a4ccddbe5c5b24ee364d14289cdee.pdf | cs.CL cs.AI cs.LG | 2023-03-13T00:00:00 | react: synergizing reasoning and acting in language models | 23 | 2,399 | 23 |
2302.11636 | Do We Really Need Complicated Model Architectures For Temporal Networks? | https://openreview.net/forum?id=ayPPc0SyLv1 | https://openreview.net/forum?id=ayPPc0SyLv1 | Weilin Cong,Si Zhang,Jian Kang,Baichuan Yuan,Hao Wu,Xin Zhou,Hanghang Tong,Mehrdad Mahdavi | ICLR 2023,Top 5% | Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning. Interestingly, we found that although both RNN and SAM could lead to a good performance, in practice neither of them is always necessary. In this paper, we prop... | https://openreview.net/pdf/4b4fffb0d6f563cba29cdcf32f829b333eb53899.pdf | cs.LG cs.AI | 2023-02-24T00:00:00 | do we really need complicated model architectures for temporal networks? | 24 | 106 | 24 |
2210.02984 | The Lie Derivative for Measuring Learned Equivariance | https://openreview.net/forum?id=JL7Va5Vy15J | https://openreview.net/forum?id=JL7Va5Vy15J | Nate Gruver,Marc Anton Finzi,Micah Goldblum,Andrew Gordon Wilson | ICLR 2023,Top 5% | Equivariance guarantees that a model's predictions capture key symmetries in data. When an image is translated or rotated, an equivariant model's representation of that image will translate or rotate accordingly. The success of convolutional neural networks has historically been tied to translation equivariance directl... | https://openreview.net/pdf/6d3e8e96475697f1cf6193df36e370ffd12302e8.pdf | cs.LG cs.AI cs.CV stat.ML | 2024-06-19T00:00:00 | the lie derivative for measuring learned equivariance | 26 | 34 | 26 |
2212.08645 | Efficient Conditionally Invariant Representation Learning | https://openreview.net/forum?id=dJruFeSRym1 | https://openreview.net/forum?id=dJruFeSRym1 | Roman Pogodin,Namrata Deka,Yazhe Li,Danica J. Sutherland,Victor Veitch,Arthur Gretton | ICLR 2023,Top 5% | We introduce the Conditional Independence Regression CovariancE (CIRCE), a measure of conditional independence for multivariate continuous-valued variables. CIRCE applies as a regularizer in settings where we wish to learn neural features $\varphi(X)$ of data $X$ to estimate a target $Y$, while being conditionally inde... | https://openreview.net/pdf/59fb48f35c3ae783e6d4bb6e29843529e56a0305.pdf | cs.LG stat.ML | 2023-12-20T00:00:00 | efficient conditionally invariant representation learning | 28 | 16 | 28 |
2210.10749 | Transformers Learn Shortcuts to Automata | https://openreview.net/forum?id=De4FYqjFueZ | https://openreview.net/forum?id=De4FYqjFueZ | Bingbin Liu,Jordan T. Ash,Surbhi Goel,Akshay Krishnamurthy,Cyril Zhang | ICLR 2023,Top 5% | Algorithmic reasoning requires capabilities which are most naturally understood through recurrent models of computation, like the Turing machine. However, Transformer models, while lacking recurrence, are able to perform such reasoning using far fewer layers than the number of reasoning steps. This raises the question:... | https://openreview.net/pdf/6fceba3e100352173ef8f64b4743424fc99f1e8d.pdf | cs.LG cs.FL stat.ML | 2023-05-03T00:00:00 | transformers learn shortcuts to automata | 30 | 154 | 30 |
2210.14215 | In-context Reinforcement Learning with Algorithm Distillation | https://openreview.net/forum?id=hy0a5MMPUv | https://openreview.net/forum?id=hy0a5MMPUv | Michael Laskin,Luyu Wang,Junhyuk Oh,Emilio Parisotto,Stephen Spencer,Richie Steigerwald,DJ Strouse,Steven Stenberg Hansen,Angelos Filos,Ethan Brooks,maxime gazeau,Himanshu Sahni,Satinder Singh,Volodymyr Mnih | ICLR 2023,Top 5% | We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model. Algorithm Distillation treats learning to reinforcement learn as an across-episode sequential prediction problem. A dataset of le... | https://openreview.net/pdf/c985c5523f4d0b869ac3914fad93d499e71fcb5a.pdf | cs.LG cs.AI | 2022-10-26T00:00:00 | in-context reinforcement learning with algorithm distillation | 31 | 119 | 31 |
2210.10394 | Near-optimal Coresets for Robust Clustering | https://openreview.net/forum?id=Nc1ZkRW8Vde | https://openreview.net/forum?id=Nc1ZkRW8Vde | Lingxiao Huang,Shaofeng H.-C. Jiang,Jianing Lou,Xuan Wu | ICLR 2023,Top 5% | We consider robust clustering problems in $\mathbb{R}^d$, specifically $k$-clustering problems (e.g., $k$-Median and $k$-Means) with $m$ \emph{outliers}, where the cost for a given center set $C \subset \mathbb{R}^d$ aggregates the distances from $C$ to all but the furthest $m$ data points, instead of all points as in ... | https://openreview.net/pdf/697bd8e4cac416b91757762ed8f0209073062f6d.pdf | cs.DS | 2022-10-20T00:00:00 | near-optimal coresets for robust clustering | 35 | 15 | 35 |
2302.04542 | Efficient Attention via Control Variates | https://openreview.net/forum?id=G-uNfHKrj46 | https://openreview.net/forum?id=G-uNfHKrj46 | Lin Zheng,Jianbo Yuan,Chong Wang,Lingpeng Kong | ICLR 2023,Top 5% | Random-feature-based attention (RFA) is an efficient approximation of softmax attention with linear runtime and space complexity. However, the approximation gap between RFA and conventional softmax attention is not well studied. Built upon previous progress of RFA, we characterize this gap through the lens of control v... | https://openreview.net/pdf/2d280a38a1ccefd5c4718511ab9b2b2571c6bd05.pdf | cs.LG cs.CL cs.CV | 2023-02-10T00:00:00 | efficient attention via control variates | 38 | 18 | 38 |
2210.01620 | SAM as an Optimal Relaxation of Bayes | https://openreview.net/forum?id=k4fevFqSQcX | https://openreview.net/forum?id=k4fevFqSQcX | Thomas Möllenhoff,Mohammad Emtiyaz Khan | ICLR 2023,Top 5% | Sharpness-aware minimization (SAM) and related adversarial deep-learning methods can drastically improve generalization, but their underlying mechanisms are not yet fully understood. Here, we establish SAM as a relaxation of the Bayes objective where the expected negative-loss is replaced by the optimal convex lower bo... | https://openreview.net/pdf/9f7784562cd53ab7d908c93bc8ece8b40dcaa922.pdf | cs.LG cs.AI math.OC stat.ML | 2023-12-12T00:00:00 | sam as an optimal relaxation of bayes | 39 | 32 | 39 |
2210.14709 | Learning on Large-scale Text-attributed Graphs via Variational Inference | https://openreview.net/forum?id=q0nmYciuuZN | https://openreview.net/forum?id=q0nmYciuuZN | Jianan Zhao,Meng Qu,Chaozhuo Li,Hao Yan,Qian Liu,Rui Li,Xing Xie,Jian Tang | ICLR 2023,Top 5% | This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very chal... | https://openreview.net/pdf/d5933681412eb0329ac9f838744d30d98d4f8c3d.pdf | cs.LG | 2023-03-02T00:00:00 | learning on large-scale text-attributed graphs via variational inference | 40 | 125 | 40 |
2301.02328 | Extreme Q-Learning: MaxEnt RL without Entropy | https://openreview.net/forum?id=SJ0Lde3tRL | https://openreview.net/forum?id=SJ0Lde3tRL | Divyansh Garg,Joey Hejna,Matthieu Geist,Stefano Ermon | ICLR 2023,Top 5% | Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Valu... | https://openreview.net/pdf/fe4a8907cc4cf7607754d21d04e1da5914902db2.pdf | cs.LG cs.AI cs.RO | 2023-03-02T00:00:00 | extreme q-learning: maxent rl without entropy | 41 | 63 | 41 |
2208.02204 | Efficiently Computing Nash Equilibria in Adversarial Team Markov Games | https://openreview.net/forum?id=mjzm6btqgV | https://openreview.net/forum?id=mjzm6btqgV | Fivos Kalogiannis,Ioannis Anagnostides,Ioannis Panageas,Emmanouil-Vasileios Vlatakis-Gkaragkounis,Vaggos Chatziafratis,Stelios Andrew Stavroulakis | ICLR 2023,Top 5% | Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, in light of computational intractability barriers in general-sum games, provable guarantees have been thus far either limited to fully competi... | https://openreview.net/pdf/3e531dec92de6b02fcbeef7a63d114423e73b571.pdf | cs.GT cs.LG cs.MA | 2022-08-04T00:00:00 | efficiently computing nash equilibria in adversarial team markov games | 42 | 13 | 42 |
2208.04933 | Simplified State Space Layers for Sequence Modeling | https://openreview.net/forum?id=Ai8Hw3AXqks | https://openreview.net/forum?id=Ai8Hw3AXqks | Jimmy T.H. Smith,Andrew Warrington,Scott Linderman | ICLR 2023,Top 5% | Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space models (SSMs), the HiPPO framework, and deep learning to achieve high performance. We build on the design of the S4 layer and introduce a new... | https://openreview.net/pdf/57b1a9f476230b4a6e75b745f2c8fe47c5fa8c5a.pdf | cs.LG | 2023-03-06T00:00:00 | simplified state space layers for sequence modeling | 43 | 478 | 43 |
2210.03115 | SimPer: Simple Self-Supervised Learning of Periodic Targets | https://openreview.net/forum?id=EKpMeEV0hOo | https://openreview.net/forum?id=EKpMeEV0hOo | Yuzhe Yang,Xin Liu,Jiang Wu,Silviu Borac,Dina Katabi,Ming-Zher Poh,Daniel McDuff | ICLR 2023,Top 5% | From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes. Due to their inherent label scarcity, learning useful representations for periodic tasks with limited or no supervision is of great benefit. Yet, existing self-supervis... | https://openreview.net/pdf/efc783fea3d58e0bcea5f077e7756fc620f0d6c2.pdf | cs.LG cs.AI cs.CV | 2023-02-22T00:00:00 | simper: simple self-supervised learning of periodic targets | 45 | 45 | 45 |
2209.06794 | PaLI: A Jointly-Scaled Multilingual Language-Image Model | https://openreview.net/forum?id=mWVoBz4W0u | https://openreview.net/forum?id=mWVoBz4W0u | Xi Chen,Xiao Wang,Soravit Changpinyo,AJ Piergiovanni,Piotr Padlewski,Daniel Salz,Sebastian Goodman,Adam Grycner,Basil Mustafa,Lucas Beyer,Alexander Kolesnikov,Joan Puigcerver,Nan Ding,Keran Rong,Hassan Akbari,Gaurav Mishra,Linting Xue,Ashish V Thapliyal,James Bradbury,Weicheng Kuo,Mojtaba Seyedhosseini,Chao Jia,Burcu K... | ICLR 2023,Top 5% | Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI, a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multi... | https://openreview.net/pdf/1870a0455d0e7a6ed7d8f02e8e156cf63f5d6b6a.pdf | cs.CV cs.CL | 2023-06-06T00:00:00 | pali: a jointly-scaled multilingual language-image model | 46 | 670 | 46 |
2205.10664 | Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks | https://openreview.net/forum?id=sWOsRj4nT1n | https://openreview.net/forum?id=sWOsRj4nT1n | Guangji Bai,Chen Ling,Liang Zhao | ICLR 2023,Top 5% | Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change. The advancement of this area is challenged by: 1) characterizing data distribution d... | https://openreview.net/pdf/5951cadc6186425d767a2acdd1f92bd01ab49268.pdf | cs.LG | 2023-02-13T00:00:00 | temporal domain generalization with drift-aware dynamic neural networks | 49 | 27 | 49 |
2302.11831 | Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement | https://openreview.net/forum?id=5N0wtJZ89r9 | https://openreview.net/forum?id=5N0wtJZ89r9 | Chongyi Li,Chun-Le Guo,man zhou,Zhexin Liang,Shangchen Zhou,Ruicheng Feng,Chen Change Loy | ICLR 2023,Top 5% | Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-light image enhancement (LLIE), especially in dealing with the intricate issue of joint luminance enhancement and noise removal while remaini... | https://openreview.net/pdf/4e2ab7acffc377a1981d0ed5d1e4310328115c82.pdf | cs.CV | 2023-02-24T00:00:00 | embedding fourier for ultra-high-definition low-light image enhancement | 52 | 91 | 52 |
2303.15015 | Towards Open Temporal Graph Neural Networks | https://openreview.net/forum?id=N9Pk5iSCzAn | https://openreview.net/forum?id=N9Pk5iSCzAn | Kaituo Feng,Changsheng Li,Xiaolu Zhang,JUN ZHOU | ICLR 2023,Top 5% | Graph neural networks (GNNs) for temporal graphs have recently attracted increasing attentions, where a common assumption is that the class set for nodes is closed. However, in real-world scenarios, it often faces the open set problem with the dynamically increased class set as the time passes by. This will bring two b... | https://openreview.net/pdf/50805c42deb9d452f3b80c28edbbd14aa21932f7.pdf | cs.LG | 2023-05-26T00:00:00 | towards open temporal graph neural networks | 55 | 15 | 55 |
2209.15430 | Relative representations enable zero-shot latent space communication | https://openreview.net/forum?id=SrC-nwieGJ | https://openreview.net/forum?id=SrC-nwieGJ | Luca Moschella,Valentino Maiorca,Marco Fumero,Antonio Norelli,Francesco Locatello,Emanuele Rodolà | ICLR 2023,Top 5% | Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the r... | https://openreview.net/pdf/2d9f62e22019d0d53476f0c4a9d760c6cc7895e2.pdf | cs.LG cs.AI | 2023-03-08T00:00:00 | relative representations enable zero-shot latent space communication | 56 | 91 | 56 |
2207.06991 | Language Modelling with Pixels | https://openreview.net/forum?id=FkSp8VW8RjH | https://openreview.net/forum?id=FkSp8VW8RjH | Phillip Rust,Jonas F. Lotz,Emanuele Bugliarello,Elizabeth Salesky,Miryam de Lhoneux,Desmond Elliott | ICLR 2023,Top 5% | Language models are defined over a finite set of inputs, which creates a vocabulary bottleneck when we attempt to scale the number of supported languages. Tackling this bottleneck results in a trade-off between what can be represented in the embedding matrix and computational issues in the output layer. This paper intr... | https://openreview.net/pdf/5ade25a9134d48be86a9acbbebf941357365462c.pdf | cs.CL cs.AI cs.CV cs.LG | 2023-04-27T00:00:00 | language modelling with pixels | 57 | 45 | 57 |
2209.15486 | Graph Neural Networks for Link Prediction with Subgraph Sketching | https://openreview.net/forum?id=m1oqEOAozQU | https://openreview.net/forum?id=m1oqEOAozQU | Benjamin Paul Chamberlain,Sergey Shirobokov,Emanuele Rossi,Fabrizio Frasca,Thomas Markovich,Nils Yannick Hammerla,Michael M. Bronstein,Max Hansmire | ICLR 2023,Top 5% | Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to count triangles (the backbone of most LP heuristics) and because they can not distinguish automorphic nodes (those having identical struct... | https://openreview.net/pdf/c24fea923ffff6f10becdc0da41b8e84eb3412a1.pdf | cs.LG cs.IR | 2023-05-03T00:00:00 | graph neural networks for link prediction with subgraph sketching | 61 | 74 | 61 |
2210.07183 | Visual Classification via Description from Large Language Models | https://openreview.net/forum?id=jlAjNL8z5cs | https://openreview.net/forum?id=jlAjNL8z5cs | Sachit Menon,Carl Vondrick | ICLR 2023,Top 5% | Vision-language models such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure -- computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the rich con... | https://openreview.net/pdf/d171255a976821dd4ebfacb7a012082c4b888b7a.pdf | cs.CV cs.LG | 2022-12-02T00:00:00 | visual classification via description from large language models | 66 | 286 | 66 |
2212.03905 | Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve | https://openreview.net/forum?id=OJ8aSjCaMNK | https://openreview.net/forum?id=OJ8aSjCaMNK | Juhan Bae,Michael R. Zhang,Michael Ruan,Eric Wang,So Hasegawa,Jimmy Ba,Roger Baker Grosse | ICLR 2023,Top 5% | Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications. In practice, VAEs usually require multiple training rounds to choose the amount of information the latent variable should retain. This trade-off between the reconstruction error (distorti... | https://openreview.net/pdf/14a6477c29547f6a0e88be838a4bb2fe39d0bef6.pdf | cs.LG cs.AI stat.ML | 2023-08-21T00:00:00 | multi-rate vae: train once, get the full rate-distortion curve | 68 | 17 | 68 |
2212.09510 | Near-optimal Policy Identification in Active Reinforcement Learning | https://openreview.net/forum?id=3OR2tbtnYC- | https://openreview.net/forum?id=3OR2tbtnYC- | Xiang Li,Viraj Mehta,Johannes Kirschner,Ian Char,Willie Neiswanger,Jeff Schneider,Andreas Krause,Ilija Bogunovic | ICLR 2023,Top 5% | Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the expensive transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often... | https://openreview.net/pdf/3f2fd20ea112039f10550e677478e83b1f6260a7.pdf | stat.ML cs.AI cs.LG | 2022-12-20T00:00:00 | near-optimal policy identification in active reinforcement learning | 69 | 6 | 69 |
2208.06073 | Conditional Antibody Design as 3D Equivariant Graph Translation | https://openreview.net/forum?id=LFHFQbjxIiP | https://openreview.net/forum?id=LFHFQbjxIiP | Xiangzhe Kong,Wenbing Huang,Yang Liu | ICLR 2023,Top 5% | Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient predic... | https://openreview.net/pdf/3ad0b04b8a9b31f816c7c80ce0cf71fad13fa636.pdf | q-bio.BM cs.LG | 2023-03-31T00:00:00 | conditional antibody design as 3d equivariant graph translation | 70 | 84 | 70 |
2302.13344 | Tailoring Language Generation Models under Total Variation Distance | https://openreview.net/forum?id=VELL0PlWfc | https://openreview.net/forum?id=VELL0PlWfc | Haozhe Ji,Pei Ke,Zhipeng Hu,Rongsheng Zhang,Minlie Huang | ICLR 2023,Top 5% | The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method. From a distributional view, MLE in fact minimizes the Kullback-Leibler divergence (KLD) between the distribution of the real data and that of the model. However, this approach forces the model to dis... | https://openreview.net/pdf/222b0c66b1d6e4c664fc67e8d5d1348ae37c505e.pdf | cs.CL | 2023-02-28T00:00:00 | tailoring language generation models under total variation distance | 72 | 18 | 72 |
2209.00588 | Transformers are Sample-Efficient World Models | https://openreview.net/forum?id=vhFu1Acb0xb | https://openreview.net/forum?id=vhFu1Acb0xb | Vincent Micheli,Eloi Alonso,François Fleuret | ICLR 2023,Top 5% | Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems. Recently, many model-based methods have been designed to address this issue, with learning in the imagination of a world model being one of the most prominent approaches. However, w... | https://openreview.net/pdf/f23ea2080e754e26ad7f8a9f9a55865dd11f0a73.pdf | cs.LG cs.AI cs.CV | 2023-03-02T00:00:00 | transformers are sample-efficient world models | 73 | 154 | 73 |
2210.00726 | Statistical Efficiency of Score Matching: The View from Isoperimetry | https://openreview.net/forum?id=TD7AnQjNzR6 | https://openreview.net/forum?id=TD7AnQjNzR6 | Frederic Koehler,Alexander Heckett,Andrej Risteski | ICLR 2023,Top 5% | Deep generative models parametrized up to a normalizing constant (e.g. energy-based models) are difficult to train by maximizing the likelihood of the data because the likelihood and/or gradients thereof cannot be explicitly or efficiently written down. Score matching is a training method, whereby instead of fitting ... | https://openreview.net/pdf/650e8b5c38872cf721fff2c0b10c3e5fa039579b.pdf | cs.LG math.ST stat.ML stat.TH | 2022-12-26T00:00:00 | statistical efficiency of score matching: the view from isoperimetry | 74 | 50 | 74 |
2205.05869 | View Synthesis with Sculpted Neural Points | https://openreview.net/forum?id=0ypGZvm0er0 | https://openreview.net/forum?id=0ypGZvm0er0 | Yiming Zuo,Jia Deng | ICLR 2023,Top 5% | We address the task of view synthesis, generating novel views of a scene given a set of images as input. In many recent works such as NeRF (Mildenhall et al., 2020), the scene geometry is parameterized using neural implicit representations (i.e., MLPs). Implicit neural representations have achieved impressive visual qu... | https://openreview.net/pdf/a844600e54c069b827ba8e0013a60b4a1193f97f.pdf | cs.CV | 2023-03-08T00:00:00 | view synthesis with sculpted neural points | 75 | 18 | 75 |
2207.02849 | Betty: An Automatic Differentiation Library for Multilevel Optimization | https://openreview.net/forum?id=LV_MeMS38Q9 | https://openreview.net/forum?id=LV_MeMS38Q9 | Sang Keun Choe,Willie Neiswanger,Pengtao Xie,Eric Xing | ICLR 2023,Top 5% | Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neural architecture search and reinforcement learning. However, gradients in MLO, which are obtained by composing best-response Jacobians via the... | https://openreview.net/pdf/e92379cd67840d63d8a85743600bfe396bcdf7fb.pdf | cs.LG cs.AI math.OC | 2023-03-16T00:00:00 | betty: an automatic differentiation library for multilevel optimization | 78 | 28 | 78 |
2302.12400 | Towards Stable Test-time Adaptation in Dynamic Wild World | https://openreview.net/forum?id=g2YraF75Tj | https://openreview.net/forum?id=g2YraF75Tj | Shuaicheng Niu,Jiaxiang Wu,Yifan Zhang,Zhiquan Wen,Yaofo Chen,Peilin Zhao,Mingkui Tan | ICLR 2023,Top 5% | Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real wor... | https://openreview.net/pdf/4bf9a568654ef33fe83fe18f5e34b489be3ca06b.pdf | cs.LG cs.CV | 2023-02-27T00:00:00 | towards stable test-time adaptation in dynamic wild world | 81 | 238 | 81 |
2303.01416 | 3D generation on ImageNet | https://openreview.net/forum?id=U2WjB9xxZ9q | https://openreview.net/forum?id=U2WjB9xxZ9q | Ivan Skorokhodov,Aliaksandr Siarohin,Yinghao Xu,Jian Ren,Hsin-Ying Lee,Peter Wonka,Sergey Tulyakov | ICLR 2023,Top 5% | All existing 3D-from-2D generators are designed for well-curated single-category datasets, where all the objects have (approximately) the same scale, 3D location, and orientation, and the camera always points to the center of the scene. This makes them inapplicable to diverse, in-the-wild datasets of non-alignable scen... | https://openreview.net/pdf/303cbc4bcfff52f24148569ddc61d7213ad090eb.pdf | cs.CV cs.AI cs.GR | 2023-03-03T00:00:00 | 3d generation on imagenet | 84 | 55 | 84 |
2301.09505 | Rethinking the Expressive Power of GNNs via Graph Biconnectivity | https://openreview.net/forum?id=r9hNv76KoT3 | https://openreview.net/forum?id=r9hNv76KoT3 | Bohang Zhang,Shengjie Luo,Liwei Wang,Di He | ICLR 2023,Top 5% | Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data. While numerous approaches have been proposed to improve GNNs with respect to the Weisfeiler-Lehman (WL) test, for most of them, there is still a lack of deep understanding of what additional power they can systematic... | https://openreview.net/pdf/be0ebeff1b3c008481709874f052f374a1d68dec.pdf | cs.LG stat.ML | 2024-02-13T00:00:00 | rethinking the expressive power of gnns via graph biconnectivity | 85 | 120 | 85 |
2206.04046 | Sparse Mixture-of-Experts are Domain Generalizable Learners | https://openreview.net/forum?id=RecZ9nB9Q4 | https://openreview.net/forum?id=RecZ9nB9Q4 | Bo Li,Yifei Shen,Jingkang Yang,Yezhen Wang,Jiawei Ren,Tong Che,Jun Zhang,Ziwei Liu | ICLR 2023,Top 5% | Human visual perception can easily generalize to out-of-distributed visual data, which is far beyond the capability of modern machine learning models. Domain generalization (DG) aims to close this gap, with existing DG methods mainly focusing on the loss function design. In this paper, we propose to explore an orthogon... | https://openreview.net/pdf/7bdb46ea980861f27d1fc50dacde68ac444c5231.pdf | cs.CV cs.AI cs.LG | 2023-01-30T00:00:00 | sparse mixture-of-experts are domain generalizable learners | 86 | 62 | 86 |
2210.09461 | Token Merging: Your ViT But Faster | https://openreview.net/forum?id=JroZRaRw7Eu | https://openreview.net/forum?id=JroZRaRw7Eu | Daniel Bolya,Cheng-Yang Fu,Xiaoliang Dai,Peizhao Zhang,Christoph Feichtenhofer,Judy Hoffman | ICLR 2023,Top 5% | We introduce Token Merging (ToMe), a simple method to increase the throughput of existing ViT models without needing to train. ToMe gradually combines similar tokens in a transformer using a general and light-weight matching algorithm that is as fast as pruning while being more accurate. Off-the-shelf, ToMe can 2x the ... | https://openreview.net/pdf/ef10c4387f0309b8f942d720fdb3ed5bc6ec5b30.pdf | cs.CV | 2023-03-03T00:00:00 | token merging: your vit but faster | 87 | 412 | 87 |
2303.01494 | Image as Set of Points | https://openreview.net/forum?id=awnvqZja69 | https://openreview.net/forum?id=awnvqZja69 | Xu Ma,Yuqian Zhou,Huan Wang,Can Qin,Bin Sun,Chang Liu,Yun Fu | ICLR 2023,Top 5% |
What is an image, and how to extract latent features?
Convolutional Networks (ConvNets) consider an image as organized pixels in a rectangular shape and extract features via convolutional operation in a local region; Vision Transformers (ViTs) treat an image as a sequence of patches and extract features via attention... | https://openreview.net/pdf/839da9c992ee84a8fa5be183d987fa55966e54ff.pdf | cs.CV | 2023-03-03T00:00:00 | image as set of points | 89 | 48 | 89 |
2212.10154 | Human-Guided Fair Classification for Natural Language Processing | https://openreview.net/forum?id=N_g8TT9Cy7f | https://openreview.net/forum?id=N_g8TT9Cy7f | Florian E. Dorner,Momchil Peychev,Nikola Konstantinov,Naman Goel,Elliott Ash,Martin Vechev | ICLR 2023,Top 25% | Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition abo... | https://openreview.net/pdf/09b5568016529de9fe0127852626c933cb6af627.pdf | cs.CL cs.AI cs.CY cs.LG | 2023-03-17T00:00:00 | human-guided fair classification for natural language processing | 90 | 2 | 90 |
2109.07867 | Humanly Certifying Superhuman Classifiers | https://openreview.net/forum?id=X5ZMzRYqUjB | https://openreview.net/forum?id=X5ZMzRYqUjB | Qiongkai Xu,Christian Walder,Chenchen Xu | ICLR 2023,Top 25% | This paper addresses a key question in current machine learning research: if we believe that a model's predictions might be better than those given by human experts, how can we (humans) verify these beliefs? In some cases, this ``superhuman'' performance is readily demonstrated; for example by defeating top-tier human ... | https://openreview.net/pdf/cd3013d0326b50c5c63ae8604d438ed46e8c664c.pdf | cs.LG cs.AI cs.CL cs.CV | 2021-09-17T00:00:00 | humanly certifying superhuman classifiers | 91 | 0 | 91 |
2108.00874 | Few-Shot Domain Adaptation For End-to-End Communication | https://openreview.net/forum?id=4F1gvduDeL | https://openreview.net/forum?id=4F1gvduDeL | Jayaram Raghuram,Yijing Zeng,Dolores Garcia,Rafael Ruiz,Somesh Jha,Joerg Widmer,Suman Banerjee | ICLR 2023,Top 25% | The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be an effective approach. A challenge faced in the practical adoption of this learning approach is that under changing channel c... | https://openreview.net/pdf/502da8335c25f515d1b0a7b57057ac446ce9f67b.pdf | cs.LG cs.IT eess.SP math.IT stat.ML | 2023-03-07T00:00:00 | few-shot domain adaptation for end-to-end communication | 92 | 4 | 92 |
2205.15769 | Concept-level Debugging of Part-Prototype Networks | https://openreview.net/forum?id=oiwXWPDTyNk | https://openreview.net/forum?id=oiwXWPDTyNk | Andrea Bontempelli,Stefano Teso,Katya Tentori,Fausto Giunchiglia,Andrea Passerini | ICLR 2023,Top 25% | Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets compute predictions based on similarity to class-specific part-prototypes learned to recognize parts of training examples, making it easy to faith... | https://openreview.net/pdf/c62dc701dcd52c5bdceeac7478072e161f7d982d.pdf | cs.LG cs.CV | 2023-01-24T00:00:00 | concept-level debugging of part-prototype networks | 95 | 51 | 95 |
2210.07082 | Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data | https://openreview.net/forum?id=JpbLyEI5EwW | https://openreview.net/forum?id=JpbLyEI5EwW | Spencer Frei,Gal Vardi,Peter Bartlett,Nathan Srebro,Wei Hu | ICLR 2023,Top 25% | The implicit biases of gradient-based optimization algorithms are conjectured to be a major factor in the success of modern deep learning. In this work, we investigate the implicit bias of gradient flow and gradient descent in two-layer fully-connected neural networks with leaky ReLU activations when the training data... | https://openreview.net/pdf/aa62c3225873e9b019b0e053bf4f2ab35a42de9c.pdf | cs.LG stat.ML | 2022-10-14T00:00:00 | implicit bias in leaky relu networks trained on high-dimensional data | 97 | 38 | 97 |
2303.01728 | Guarded Policy Optimization with Imperfect Online Demonstrations | https://openreview.net/forum?id=O5rKg7IRQIO | https://openreview.net/forum?id=O5rKg7IRQIO | Zhenghai Xue,Zhenghao Peng,Quanyi Li,Zhihan Liu,Bolei Zhou | ICLR 2023,Top 25% | The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrations. Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, p... | https://openreview.net/pdf/e19dee281e43ab70ef8f8640d6ccb689bed45bd8.pdf | cs.LG cs.AI cs.RO | 2023-04-25T00:00:00 | guarded policy optimization with imperfect online demonstrations | 98 | 10 | 98 |
2403.00329 | Learning with Logical Constraints but without Shortcut Satisfaction | https://openreview.net/forum?id=M2unceRvqhh | https://openreview.net/forum?id=M2unceRvqhh | Zenan Li,Zehua Liu,Yuan Yao,Jingwei Xu,Taolue Chen,Xiaoxing Ma,Jian L\"{u} | ICLR 2023,Top 25% | Recent studies have started to explore the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the knowledge. In this paper, we present ... | https://openreview.net/pdf/172ef390502d417f43730d591512cda9247cb5fa.pdf | cs.AI cs.LG | 2024-03-04T00:00:00 | learning with logical constraints but without shortcut satisfaction | 99 | 18 | 99 |
2210.04871 | Certified Training: Small Boxes are All You Need | https://openreview.net/forum?id=7oFuxtJtUMH | https://openreview.net/forum?id=7oFuxtJtUMH | Mark Niklas Mueller,Franziska Eckert,Marc Fischer,Martin Vechev | ICLR 2023,Top 25% | To obtain, deterministic guarantees of adversarial robustness, specialized training methods are used. We propose, SABR, a novel such certified training method, based on the key insight that propagating interval bounds for a small but carefully selected subset of the adversarial input region is sufficient to approximate... | https://openreview.net/pdf/e61a2e061488e943012e445b9549adde476fd159.pdf | cs.LG cs.CR | 2023-03-10T00:00:00 | certified training: small boxes are all you need | 100 | 45 | 100 |
2210.02441 | Ask Me Anything: A simple strategy for prompting language models | https://openreview.net/forum?id=bhUPJnS2g0X | https://openreview.net/forum?id=bhUPJnS2g0X | Simran Arora,Avanika Narayan,Mayee F Chen,Laurel Orr,Neel Guha,Kush Bhatia,Ines Chami,Christopher Re | ICLR 2023,Top 25% | Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore ... | https://openreview.net/pdf/5b1bcdac167fa4b294480f303ac3722afa8a9aac.pdf | cs.CL | 2022-11-22T00:00:00 | ask me anything: a simple strategy for prompting language models | 106 | 201 | 106 |
2209.12288 | On Representing Linear Programs by Graph Neural Networks | https://openreview.net/forum?id=cP2QVK-uygd | https://openreview.net/forum?id=cP2QVK-uygd | Ziang Chen,Jialin Liu,Xinshang Wang,Wotao Yin | ICLR 2023,Top 25% | Learning to optimize is a rapidly growing area that aims to solve optimization problems or improve existing optimization algorithms using machine learning (ML). In particular, the graph neural network (GNN) is considered a suitable ML model for optimization problems whose variables and constraints are permutation--inva... | https://openreview.net/pdf/155020e920d47414c7089209e49eaadf7b34a960.pdf | cs.LG math.OC | 2023-05-29T00:00:00 | on representing linear programs by graph neural networks | 107 | 31 | 107 |
2302.14372 | The In-Sample Softmax for Offline Reinforcement Learning | https://openreview.net/forum?id=u-RuvyDYqCM | https://openreview.net/forum?id=u-RuvyDYqCM | Chenjun Xiao,Han Wang,Yangchen Pan,Adam White,Martha White | ICLR 2023,Top 25% | Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however, is that the bootstrapping update underlying many of our methods suffers from insufficient action-coverage: standard max operator may select ... | https://openreview.net/pdf/69f475d9352b20ebc3fc03da590f54192f7856ec.pdf | cs.LG cs.AI | 2023-04-20T00:00:00 | the in-sample softmax for offline reinforcement learning | 112 | 26 | 112 |
2303.03023 | Guiding Energy-based Models via Contrastive Latent Variables | https://openreview.net/forum?id=CZmHHj9MgkP | https://openreview.net/forum?id=CZmHHj9MgkP | Hankook Lee,Jongheon Jeong,Sejun Park,Jinwoo Shin | ICLR 2023,Top 25% | An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unstable and time-consuming. In recent years, various training techniques have been developed, e.g., better divergence measures or stabilization... | https://openreview.net/pdf/9c51d101c5d336bf5bc034b2876d79796069ac59.pdf | cs.LG cs.CV | 2023-03-07T00:00:00 | guiding energy-based models via contrastive latent variables | 114 | 14 | 114 |
2302.01384 | Energy-Inspired Self-Supervised Pretraining for Vision Models | https://openreview.net/forum?id=ZMz-sW6gCLF | https://openreview.net/forum?id=ZMz-sW6gCLF | Ze Wang,Jiang Wang,Zicheng Liu,Qiang Qiu | ICLR 2023,Top 25% | Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy e... | https://openreview.net/pdf/36e3907af9186c722c512ea75c280aaae585101e.pdf | cs.CV | 2023-02-06T00:00:00 | energy-inspired self-supervised pretraining for vision models | 117 | 8 | 117 |
2210.02875 | Binding Language Models in Symbolic Languages | https://openreview.net/forum?id=lH1PV42cbF | https://openreview.net/forum?id=lH1PV42cbF | Zhoujun Cheng,Tianbao Xie,Peng Shi,Chengzu Li,Rahul Nadkarni,Yushi Hu,Caiming Xiong,Dragomir Radev,Mari Ostendorf,Luke Zettlemoyer,Noah A. Smith,Tao Yu | ICLR 2023,Top 25% | Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) fu... | https://openreview.net/pdf/d226e827fb59bcd4253c7eb8ce07d339ef5d519d.pdf | cs.CL | 2023-03-02T00:00:00 | binding language models in symbolic languages | 118 | 195 | 118 |
2303.16194 | BC-IRL: Learning Generalizable Reward Functions from Demonstrations | https://openreview.net/forum?id=Ovnwe_sDQW | https://openreview.net/forum?id=Ovnwe_sDQW | Andrew Szot,Amy Zhang,Dhruv Batra,Zsolt Kira,Franziska Meier | ICLR 2023,Top 25% | How well do reward functions learned with inverse reinforcement learning (IRL) generalize? We illustrate that state-of-the-art IRL algorithms, which maximize a maximum-entropy objective, learn rewards that overfit to the demonstrations. Such rewards struggle to provide meaningful rewards for states not covered by the d... | https://openreview.net/pdf/214dd3d4f346964ae17621ab8b33fe8cd5a4a444.pdf | cs.LG | 2023-03-29T00:00:00 | bc-irl: learning generalizable reward functions from demonstrations | 120 | 8 | 120 |
2206.05564 | gDDIM: Generalized denoising diffusion implicit models | https://openreview.net/forum?id=1hKE9qjvz- | https://openreview.net/forum?id=1hKE9qjvz- | Qinsheng Zhang,Molei Tao,Yongxin Chen | ICLR 2023,Top 25% | Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can be obtained by u... | https://openreview.net/pdf/101656f96f6c22373cb9bf570b89250c966aedd5.pdf | cs.LG | 2023-03-24T00:00:00 | gddim: generalized denoising diffusion implicit models | 122 | 111 | 122 |
2301.09604 | FedExP: Speeding Up Federated Averaging via Extrapolation | https://openreview.net/forum?id=IPrzNbddXV | https://openreview.net/forum?id=IPrzNbddXV | Divyansh Jhunjhunwala,Shiqiang Wang,Gauri Joshi | ICLR 2023,Top 25% | Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees combined with secure aggregation. Recent work has sought to generalize the vanilla averaging in FedAvg to a generalized gradient descent ste... | https://openreview.net/pdf/8f9800051e3387ff23fc9a42a792d5ace5e665aa.pdf | cs.LG | 2023-03-07T00:00:00 | fedexp: speeding up federated averaging via extrapolation | 123 | 49 | 123 |
2210.07839 | Contrastive Audio-Visual Masked Autoencoder | https://openreview.net/forum?id=QPtMRyk5rb | https://openreview.net/forum?id=QPtMRyk5rb | Yuan Gong,Andrew Rouditchenko,Alexander H. Liu,David Harwath,Leonid Karlinsky,Hilde Kuehne,James R. Glass | ICLR 2023,Top 25% | In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks... | https://openreview.net/pdf/6de0262994e10ffd87b06b9ad0e8b4f86c84f044.pdf | cs.MM cs.CV cs.SD eess.AS | 2023-04-13T00:00:00 | contrastive audio-visual masked autoencoder | 126 | 117 | 126 |
2210.03820 | The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks | https://openreview.net/forum?id=IM4xp7kGI5V | https://openreview.net/forum?id=IM4xp7kGI5V | Daniel Kunin,Atsushi Yamamura,Chao Ma,Surya Ganguli | ICLR 2023,Top 25% | In this work, we explore the maximum-margin bias of quasi-homogeneous neural networks trained with gradient flow on an exponential loss and past a point of separability. We introduce the class of quasi-homogeneous models, which is expressive enough to describe nearly all neural networks with homogeneous activations, ev... | https://openreview.net/pdf/35e901e92e53dbfee861403dfbe3d0044bfb91a7.pdf | cs.LG stat.ML | 2023-02-20T00:00:00 | the asymmetric maximum margin bias of quasi-homogeneous neural networks | 127 | 20 | 127 |
2303.13971 | Optimal Transport for Offline Imitation Learning | https://openreview.net/forum?id=MhuFzFsrfvH | https://openreview.net/forum?id=MhuFzFsrfvH | Yicheng Luo,zhengyao jiang,Samuel Cohen,Edward Grefenstette,Marc Peter Deisenroth | ICLR 2023,Top 25% | With the advent of large datasets, offline reinforcement learning is a promising framework for learning good decision-making policies without the need to interact with the real environment.
However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering is di... | https://openreview.net/pdf/3c2503af4f49d5f2f79a720075d8cfc042c50960.pdf | cs.LG | 2023-03-27T00:00:00 | optimal transport for offline imitation learning | 128 | 26 | 128 |
2303.02984 | Learning multi-scale local conditional probability models of images | https://openreview.net/forum?id=VZX2I_VVJKH | https://openreview.net/forum?id=VZX2I_VVJKH | Zahra Kadkhodaie,Florentin Guth,Stéphane Mallat,Eero P Simoncelli | ICLR 2023,Top 25% | Deep neural networks can learn powerful prior probability models for images, as evidenced by the high-quality generations obtained with recent score-based diffusion methods. But the means by which these networks capture complex global statistical structure, apparently without suffering from the curse of dimensionality,... | https://openreview.net/pdf/df0e89dd7728d64bf15f64a0771ede4c857aad7c.pdf | cs.CV cs.LG | 2023-03-07T00:00:00 | learning multi-scale local conditional probability models of images | 130 | 17 | 130 |
2212.03574 | Learning rigid dynamics with face interaction graph networks | https://openreview.net/forum?id=J7Uh781A05p | https://openreview.net/forum?id=J7Uh781A05p | Kelsey R Allen,Yulia Rubanova,Tatiana Lopez-Guevara,William F Whitney,Alvaro Sanchez-Gonzalez,Peter Battaglia,Tobias Pfaff | ICLR 2023,Top 25% | Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical dynamics, such as fluids, cloth and articulated bodies, they have bee... | https://openreview.net/pdf/ce59b39a6e91536851b8532e573580c0b40de1fc.pdf | cs.LG | 2022-12-08T00:00:00 | learning rigid dynamics with face interaction graph networks | 132 | 34 | 132 |
2304.01063 | Depth Separation with Multilayer Mean-Field Networks | https://openreview.net/forum?id=uzFQpkEzOo | https://openreview.net/forum?id=uzFQpkEzOo | Yunwei Ren,Mo Zhou,Rong Ge | ICLR 2023,Top 25% | Depth separation—why a deeper network is more powerful than a shallow one—has been a major problem in deep learning theory. Previous results often focus on representation power, for example, Safran et al. (2019) constructed a function that is easy to approximate using a 3-layer network but not approximable by any 2-lay... | https://openreview.net/pdf/ceeae99121e997ccdd6046c0ebad91895125624b.pdf | cs.LG math.OC | 2023-04-04T00:00:00 | depth separation with multilayer mean-field networks | 134 | 3 | 134 |
2405.10939 | DINO as a von Mises-Fisher mixture model | https://openreview.net/forum?id=cMJo1FTwBTQ | https://openreview.net/forum?id=cMJo1FTwBTQ | Hariprasath Govindarajan,Per Sidén,Jacob Roll,Fredrik Lindsten | ICLR 2023,Top 25% | Self-distillation methods using Siamese networks are popular for self-supervised pre-training. DINO is one such method based on a cross-entropy loss between $K$-dimensional probability vectors, obtained by applying a softmax function to the dot product between representations and learnt prototypes. Given the fact that ... | https://openreview.net/pdf/7c0fa9125fa53c842a7c216fd9f1b16ee517710f.pdf | cs.LG cs.AI cs.CV | 2024-05-20T00:00:00 | dino as a von mises-fisher mixture model | 137 | 11 | 137 |
2301.09816 | SMART: Self-supervised Multi-task pretrAining with contRol Transformers | https://openreview.net/forum?id=9piH3Hg8QEf | https://openreview.net/forum?id=9piH3Hg8QEf | Yanchao Sun,Shuang Ma,Ratnesh Madaan,Rogerio Bonatti,Furong Huang,Ashish Kapoor | ICLR 2023,Top 25% | Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining representations without explicit labels. When it comes to sequential decision-making tasks, however, it is difficult to properly design such a p... | https://openreview.net/pdf/0bed689d4b0c72cb2f2561862218853290e48ce5.pdf | cs.LG cs.AI | 2023-01-25T00:00:00 | smart: self-supervised multi-task pretraining with control transformers | 139 | 39 | 139 |
2207.11177 | Provable Defense Against Geometric Transformations | https://openreview.net/forum?id=ThXqBsRI-cY | https://openreview.net/forum?id=ThXqBsRI-cY | Rem Yang,Jacob Laurel,Sasa Misailovic,Gagandeep Singh | ICLR 2023,Top 25% | Geometric image transformations that arise in the real world, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to be certifiably robust to these perturbations is critical. However, no prior work has been able to incorporate the objective of deterministic ... | https://openreview.net/pdf/5f45f6724426cb1196f8d2e3bd7a227feec2b203.pdf | cs.LG cs.CV | 2023-05-09T00:00:00 | provable defense against geometric transformations | 141 | 15 | 141 |
2210.09520 | Using Language to Extend to Unseen Domains | https://openreview.net/forum?id=eR2dG8yjnQ | https://openreview.net/forum?id=eR2dG8yjnQ | Lisa Dunlap,Clara Mohri,Devin Guillory,Han Zhang,Trevor Darrell,Joseph E. Gonzalez,Aditi Raghunathan,Anna Rohrbach | ICLR 2023,Top 25% | It is expensive to collect training data for every possible domain that a vision model may encounter when deployed. We instead consider how simply $\textit{verbalizing}$ the training domain (e.g.``photos of birds'') as well as domains we want to extend to but do not have data for (e.g.``paintings of birds'') can improv... | https://openreview.net/pdf/bb5314efba6d37a2ea4f8cdbdeccd9351dde3016.pdf | cs.CV | 2023-05-02T00:00:00 | using language to extend to unseen domains | 147 | 35 | 147 |
2303.03095 | Can We Find Nash Equilibria at a Linear Rate in Markov Games? | https://openreview.net/forum?id=eQzLwwGyQrb | https://openreview.net/forum?id=eQzLwwGyQrb | Zhuoqing Song,Jason D. Lee,Zhuoran Yang | ICLR 2023,Top 25% | We study decentralized learning in two-player zero-sum discounted Markov games where the goal is to design a policy optimization algorithm for either agent satisfying two properties. First, the player does not need to know the policy of the opponent to update its policy. Second, when both players adopt the algorithm, t... | https://openreview.net/pdf/f6285c79bac699974cbfa7334d3e42800b338c64.pdf | cs.GT cs.LG math.OC | 2023-03-07T00:00:00 | can we find nash equilibria at a linear rate in markov games? | 148 | 8 | 148 |
2209.11883 | Hebbian Deep Learning Without Feedback | https://openreview.net/forum?id=8gd4M-_Rj1 | https://openreview.net/forum?id=8gd4M-_Rj1 | Adrien Journé,Hector Garcia Rodriguez,Qinghai Guo,Timoleon Moraitis | ICLR 2023,Top 25% | Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitf... | https://openreview.net/pdf/8069b75c93174254f8042cc114dad9bbd5b73989.pdf | cs.NE cs.LG q-bio.NC | 2023-08-04T00:00:00 | hebbian deep learning without feedback | 149 | 46 | 149 |
2310.16835 | Proposal-Contrastive Pretraining for Object Detection from Fewer Data | https://openreview.net/forum?id=gm0VZ-h-hPy | https://openreview.net/forum?id=gm0VZ-h-hPy | Quentin Bouniot,Romaric Audigier,Angelique Loesch,Amaury Habrard | ICLR 2023,Top 25% | The use of pretrained deep neural networks represents an attractive way to achieve strong results with few data available. When specialized in dense problems such as object detection, learning local rather than global information in images has proven to be more efficient. However, for unsupervised pretraining, the popu... | https://openreview.net/pdf/409d076ce5528382d9695b832fdb2bebf4977300.pdf | cs.CV cs.AI cs.LG | 2023-10-26T00:00:00 | proposal-contrastive pretraining for object detection from fewer data | 152 | 2 | 152 |
2211.13350 | Choreographer: Learning and Adapting Skills in Imagination | https://openreview.net/forum?id=PhkWyijGi5b | https://openreview.net/forum?id=PhkWyijGi5b | Pietro Mazzaglia,Tim Verbelen,Bart Dhoedt,Alexandre Lacoste,Sai Rajeswar | ICLR 2023,Top 25% | Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the environment. However, without appropriate knowledge and exploration, skills may provide control only over a restricted area of the environment,... | https://openreview.net/pdf/3b9c0c356a7760d7f70096b567ff2aef51b26f98.pdf | cs.AI cs.LG | 2024-01-22T00:00:00 | choreographer: learning and adapting skills in imagination | 155 | 21 | 155 |
2301.13261 | Emergence of Maps in the Memories of Blind Navigation Agents | https://openreview.net/forum?id=lTt4KjHSsyl | https://openreview.net/forum?id=lTt4KjHSsyl | Erik Wijmans,Manolis Savva,Irfan Essa,Stefan Lee,Ari S. Morcos,Dhruv Batra | ICLR 2023,Top 25% | Animal navigation research posits that organisms build and maintain internal spa- tial representations, or maps, of their environment. We ask if machines – specifically, artificial intelligence (AI) navigation agents – also build implicit (or ‘mental’) maps. A positive answer to this question would (a) explain the surp... | https://openreview.net/pdf/6aff51942ab3664378283e5da2b36db1cd04db62.pdf | cs.AI cs.CV cs.LG cs.RO | 2023-02-01T00:00:00 | emergence of maps in the memories of blind navigation agents | 158 | 27 | 158 |
2210.00643 | Spectral Augmentation for Self-Supervised Learning on Graphs | https://openreview.net/forum?id=DjzBCrMBJ_p | https://openreview.net/forum?id=DjzBCrMBJ_p | Lu Lin,Jinghui Chen,Hongning Wang | ICLR 2023,Top 25% | Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily relies on graph augmentation to reflect invariant patterns that are robust to small perturbations; yet it still remains unclear about what gra... | https://openreview.net/pdf/f3bc720be318c5e2d1b97759ef657ead63c87974.pdf | cs.LG cs.AI | 2023-06-22T00:00:00 | spectral augmentation for self-supervised learning on graphs | 159 | 47 | 159 |
2208.00789 | Self-supervised learning with rotation-invariant kernels | https://openreview.net/forum?id=8uu6JStuYm | https://openreview.net/forum?id=8uu6JStuYm | Léon Zheng,Gilles Puy,Elisa Riccietti,Patrick Perez,Rémi Gribonval | ICLR 2023,Top 25% | We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learning of image representations. Besides being fully competitive with the state of the art, our method significantly reduces time and memory com... | https://openreview.net/pdf/ef737455df9ff0ef5ebc958acb84991ccf4647e6.pdf | cs.CV cs.AI | 2023-03-09T00:00:00 | self-supervised learning with rotation-invariant kernels | 161 | 2 | 161 |
2210.04886 | Revisiting adapters with adversarial training | https://openreview.net/forum?id=HPdxC1THU8T | https://openreview.net/forum?id=HPdxC1THU8T | Sylvestre-Alvise Rebuffi,Francesco Croce,Sven Gowal | ICLR 2023,Top 25% | While adversarial training is generally used as a defense mechanism, recent works show that it can also act as a regularizer. By co-training a neural network on clean and adversarial inputs, it is possible to improve classification accuracy on the clean, non-adversarial inputs. We demonstrate that, contrary to previous... | https://openreview.net/pdf/c986093cab366dcc82865df98b5906e39dc7c493.pdf | cs.CV cs.LG | 2022-10-11T00:00:00 | revisiting adapters with adversarial training | 166 | 16 | 166 |
2304.02786 | UNICORN: A Unified Backdoor Trigger Inversion Framework | https://openreview.net/forum?id=Mj7K4lglGyj | https://openreview.net/forum?id=Mj7K4lglGyj | Zhenting Wang,Kai Mei,Juan Zhai,Shiqing Ma | ICLR 2023,Top 25% | The backdoor attack, where the adversary uses inputs stamped with triggers (e.g., a patch) to activate pre-planted malicious behaviors, is a severe threat to Deep Neural Network (DNN) models. Trigger inversion is an effective way of identifying backdoor models and understanding embedded adversarial behaviors. A challen... | https://openreview.net/pdf/edd35173abda536a0bd486d49c34c8ce04e56652.pdf | cs.LG cs.AI cs.CR cs.CV | 2023-04-07T00:00:00 | unicorn: a unified backdoor trigger inversion framework | 167 | 43 | 167 |
2206.04192 | ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion | https://openreview.net/forum?id=xkev3_np08z | https://openreview.net/forum?id=xkev3_np08z | Aleksandar Pavlović,Emanuel Sallinger | ICLR 2023,Top 25% | Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). KG embedding models (KGEs) have yielded promising results for KGC, yet any current KGE is i... | https://openreview.net/pdf/071ed2e450ebd00e88fdcae80a0773cfe4c7aec8.pdf | cs.LG cs.AI | 2023-03-23T00:00:00 | expressive: a spatio-functional embedding for knowledge graph completion | 168 | 16 | 168 |
2210.16140 | Localized Randomized Smoothing for Collective Robustness Certification | https://openreview.net/forum?id=-k7Lvk0GpBl | https://openreview.net/forum?id=-k7Lvk0GpBl | Jan Schuchardt,Tom Wollschläger,Aleksandar Bojchevski,Stephan Günnemann | ICLR 2023,Top 25% | Models for image segmentation, node classification and many other tasks map a single input to multiple labels. By perturbing this single shared input (e.g. the image) an adversary can manipulate several predictions (e.g. misclassify several pixels). Collective robustness certification is the task of provably bounding t... | https://openreview.net/pdf/2c33160f207d6fbfbc89af90d5f1b6d98446dab7.pdf | cs.LG cs.CV | 2024-02-27T00:00:00 | localized randomized smoothing for collective robustness certification | 169 | 9 | 169 |
2211.10257 | Model-based Causal Bayesian Optimization | https://openreview.net/forum?id=Vk-34OQ7rFo | https://openreview.net/forum?id=Vk-34OQ7rFo | Scott Sussex,Anastasia Makarova,Andreas Krause | ICLR 2023,Top 25% | How should we intervene on an unknown structural equation model to maximize a downstream variable of interest? This setting, also known as causal Bayesian optimization (CBO), has important applications in medicine, ecology, and manufacturing. Standard Bayesian optimization algorithms fail to effectively leverage the un... | https://openreview.net/pdf/4d05ca91171a6278984e77236a0ead44b9d44e48.pdf | cs.LG stat.ML | 2023-03-13T00:00:00 | model-based causal bayesian optimization | 171 | 23 | 171 |
2302.04496 | Dual Algorithmic Reasoning | https://openreview.net/forum?id=hhvkdRdWt1F | https://openreview.net/forum?id=hhvkdRdWt1F | Danilo Numeroso,Davide Bacciu,Petar Veličković | ICLR 2023,Top 25% | Neural Algorithmic Reasoning is an emerging area of machine learning which seeks to infuse algorithmic computation in neural networks, typically by training neural models to approximate steps of classical algorithms. In this context, much of the current work has focused on learning reachability and shortest path graph ... | https://openreview.net/pdf/68736260b81982cea120df8994f055abbfe1ec5c.pdf | cs.LG cs.DS | 2023-02-10T00:00:00 | dual algorithmic reasoning | 173 | 16 | 173 |
2406.13781 | A Primal-Dual Framework for Transformers and Neural Networks | https://openreview.net/forum?id=U_T8-5hClV | https://openreview.net/forum?id=U_T8-5hClV | Tan Minh Nguyen,Tam Minh Nguyen,Nhat Ho,Andrea L. Bertozzi,Richard Baraniuk,Stanley Osher | ICLR 2023,Top 25% | Self-attention is key to the remarkable success of transformers in sequence modeling tasks including many applications in natural language processing and computer vision. Like neural network layers, these attention mechanisms are often developed by heuristics and experience. To provide a principled framework for constr... | https://openreview.net/pdf/ea60565f7f50777889e3d7d4e95d5feb7f8df5cb.pdf | cs.LG cs.AI cs.CL cs.CV stat.ML | 2024-06-21T00:00:00 | a primal-dual framework for transformers and neural networks | 174 | 13 | 174 |
2202.11202 | Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning | https://openreview.net/forum?id=f0a_dWEYg-Td | https://openreview.net/forum?id=f0a_dWEYg-Td | Hao He,Kaiwen Zha,Dina Katabi | ICLR 2023,Top 25% | Indiscriminate data poisoning attacks are quite effective against supervised learning. However, not much is known about their impact on unsupervised contrastive learning (CL). This paper is the first to consider indiscriminate poisoning attacks of contrastive learning. We propose Contrastive Poisoning (CP), the first e... | https://openreview.net/pdf/06017158fae111af5eb2c5e44b5b2c0c9a8d4526.pdf | cs.LG cs.AI cs.CR cs.CV | 2023-03-10T00:00:00 | indiscriminate poisoning attacks on unsupervised contrastive learning | 182 | 31 | 182 |
2211.10445 | Building a Subspace of Policies for Scalable Continual Learning | https://openreview.net/forum?id=UKr0MwZM6fL | https://openreview.net/forum?id=UKr0MwZM6fL | Jean-Baptiste Gaya,Thang Doan,Lucas Caccia,Laure Soulier,Ludovic Denoyer,Roberta Raileanu | ICLR 2023,Top 25% | The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size models that scale poorly with the number of tasks. In this work, we aim to strike a ... | https://openreview.net/pdf/ab8b649c5427a1281f061c035a6c4c3a82699f57.pdf | cs.LG cs.AI | 2023-03-03T00:00:00 | building a subspace of policies for scalable continual learning | 184 | 29 | 184 |
2208.14057 | Symmetric Pruning in Quantum Neural Networks | https://openreview.net/forum?id=K96AogLDT2K | https://openreview.net/forum?id=K96AogLDT2K | Xinbiao Wang,Junyu Liu,Tongliang Liu,Yong Luo,Yuxuan Du,Dacheng Tao | ICLR 2023,Top 25% | Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leadin... | https://openreview.net/pdf/985ba693c6dc7d26909831bb66906ae4f3810a91.pdf | quant-ph cs.AI cs.LG | 2023-04-11T00:00:00 | symmetric pruning in quantum neural networks | 187 | 23 | 187 |
2203.10991 | Minimum Variance Unbiased N:M Sparsity for the Neural Gradients | https://openreview.net/forum?id=vuD2xEtxZcj | https://openreview.net/forum?id=vuD2xEtxZcj | Brian Chmiel,Itay Hubara,Ron Banner,Daniel Soudry | ICLR 2023,Top 25% | In deep learning, fine-grained N:M sparsity reduces the data footprint and bandwidth of a General Matrix multiply (GEMM) up to x2, and doubles throughput by skipping computation of zero values. So far, it was mainly only used to prune weights to accelerate the forward and backward phases. We examine how this method ca... | https://openreview.net/pdf/b7b54047fdaf97f505713a0b6675abc7e120c460.pdf | cs.LG cs.AI | 2024-06-11T00:00:00 | minimum variance unbiased n:m sparsity for the neural gradients | 188 | 10 | 188 |
2210.02747 | Flow Matching for Generative Modeling | https://openreview.net/forum?id=PqvMRDCJT9t | https://openreview.net/forum?id=PqvMRDCJT9t | Yaron Lipman,Ricky T. Q. Chen,Heli Ben-Hamu,Maximilian Nickel,Matthew Le | ICLR 2023,Top 25% | We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability p... | https://openreview.net/pdf/e99034416acd1ca82991f5d63735e77130fc06a7.pdf | cs.LG cs.AI stat.ML | 2023-02-09T00:00:00 | flow matching for generative modeling | 192 | 1,026 | 192 |
2212.07919 | ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning | https://openreview.net/forum?id=xYlJRpzZtsY | https://openreview.net/forum?id=xYlJRpzZtsY | Olga Golovneva,Moya Peng Chen,Spencer Poff,Martin Corredor,Luke Zettlemoyer,Maryam Fazel-Zarandi,Asli Celikyilmaz | ICLR 2023,Top 25% | Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult withou... | https://openreview.net/pdf/3f6164615b8f835462171508e65f188740d76ee8.pdf | cs.CL cs.LG | 2023-09-13T00:00:00 | roscoe: a suite of metrics for scoring step-by-step reasoning | 197 | 134 | 197 |
2207.05987 | DocPrompting: Generating Code by Retrieving the Docs | https://openreview.net/forum?id=ZTCxT2t2Ru | https://openreview.net/forum?id=ZTCxT2t2Ru | Shuyan Zhou,Uri Alon,Frank F. Xu,Zhengbao Jiang,Graham Neubig | ICLR 2023,Top 25% | Publicly available source-code libraries are continuously growing and changing. This makes it impossible for models of code
to keep current with all available APIs by simply training these models on existing code repositories. Thus, existing models inherently cannot generalize to using unseen functions and libraries, b... | https://openreview.net/pdf/c9881a374e0bce9d005809d63e83dfdae53d9d40.pdf | cs.CL cs.AI cs.SE | 2023-02-21T00:00:00 | docprompting: generating code by retrieving the docs | 201 | 125 | 201 |
2301.05217 | Progress measures for grokking via mechanistic interpretability | https://openreview.net/forum?id=9XFSbDPmdW | https://openreview.net/forum?id=9XFSbDPmdW | Neel Nanda,Lawrence Chan,Tom Lieberum,Jess Smith,Jacob Steinhardt | ICLR 2023,Top 25% | Neural networks often exhibit emergent behavior in which qualitatively new capabilities that arise from scaling up the number of parameters, training data, or even the number of steps. One approach to understanding emergence is to find the continuous \textit{progress measures} that underlie the seemingly discontinuous ... | https://openreview.net/pdf/4a139897d29f8bd1c37ac9483d9e6ac2fa5ec8fb.pdf | cs.LG cs.AI | 2023-10-23T00:00:00 | progress measures for grokking via mechanistic interpretability | 203 | 377 | 203 |
2209.12643 | PiFold: Toward effective and efficient protein inverse folding | https://openreview.net/forum?id=oMsN9TYwJ0j | https://openreview.net/forum?id=oMsN9TYwJ0j | Zhangyang Gao,Cheng Tan,Stan Z. Li | ICLR 2023,Top 25% | How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attention in recent years; however, few methods can simultaneously improve the accuracy and efficiency due to the lack of expressive features and a... | https://openreview.net/pdf/e1a0ac295d7e905fb72e78968e396731ad364a0b.pdf | cs.AI cs.CE cs.LG | 2023-04-14T00:00:00 | pifold: toward effective and efficient protein inverse folding | 204 | 104 | 204 |
2303.13002 | Planning Goals for Exploration | https://openreview.net/forum?id=6qeBuZSo7Pr | https://openreview.net/forum?id=6qeBuZSo7Pr | Edward S. Hu,Richard Chang,Oleh Rybkin,Dinesh Jayaraman | ICLR 2023,Top 25% | Dropped into an unknown environment, what should an agent do to quickly learn about the environment and how to accomplish diverse tasks within it? We address this question within the goal-conditioned reinforcement learning paradigm, by identifying how the agent should set its goals at training time to maximize explorat... | https://openreview.net/pdf/b28237bb9e4d96d5f02a9d0639565db68727d08c.pdf | cs.LG cs.AI cs.RO | 2023-03-24T00:00:00 | planning goals for exploration | 205 | 23 | 205 |
2303.08133 | MeshDiffusion: Score-based Generative 3D Mesh Modeling | https://openreview.net/forum?id=0cpM2ApF9p6 | https://openreview.net/forum?id=0cpM2ApF9p6 | Zhen Liu,Yao Feng,Michael J. Black,Derek Nowrouzezahrai,Liam Paull,Weiyang Liu | ICLR 2023,Top 25% | We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulat... | https://openreview.net/pdf/f4b27531cf7771c608830f2a184f9c5ef06eab1c.pdf | cs.GR cs.AI cs.CV cs.LG | 2023-04-18T00:00:00 | meshdiffusion: score-based generative 3d mesh modeling | 207 | 146 | 207 |
2405.16601 | A CMDP-within-online framework for Meta-Safe Reinforcement Learning | https://openreview.net/forum?id=mbxz9Cjehr | https://openreview.net/forum?id=mbxz9Cjehr | Vanshaj Khattar,Yuhao Ding,Bilgehan Sel,Javad Lavaei,Ming Jin | ICLR 2023,Top 25% | Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has not been adequately addressed in the existing works, making their application restricted in real-world settings. In this paper, we study the p... | https://openreview.net/pdf/a0814d04508ed834d5ecec6097573946c1f8b619.pdf | cs.LG | 2024-05-28T00:00:00 | a cmdp-within-online framework for meta-safe reinforcement learning | 210 | 12 | 210 |
2204.09297 | Effects of Graph Convolutions in Multi-layer Networks | https://openreview.net/forum?id=P-73JPgRs0R | https://openreview.net/forum?id=P-73JPgRs0R | Aseem Baranwal,Kimon Fountoulakis,Aukosh Jagannath | ICLR 2023,Top 25% | Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi-layer networks. We study these effects through the node classif... | https://openreview.net/pdf/d210fced5bf1ca06dc521b5bd8088e97ffbdc31e.pdf | cs.LG stat.ML | 2022-08-03T00:00:00 | effects of graph convolutions in multi-layer networks | 211 | 23 | 211 |
2205.15480 | Post-hoc Concept Bottleneck Models | https://openreview.net/forum?id=nA5AZ8CEyow | https://openreview.net/forum?id=nA5AZ8CEyow | Mert Yuksekgonul,Maggie Wang,James Zou | ICLR 2023,Top 25% | Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model "sees" in an input and which of these concepts are deemed ... | https://openreview.net/pdf/bd9522b16fb6b3a1e89ec20c6aa411c7a84f0fb3.pdf | cs.LG cs.AI stat.ML | 2023-02-03T00:00:00 | post-hoc concept bottleneck models | 212 | 181 | 212 |
2301.13381 | When Source-Free Domain Adaptation Meets Learning with Noisy Labels | https://openreview.net/forum?id=u2Pd6x794I | https://openreview.net/forum?id=u2Pd6x794I | Li Yi,Gezheng Xu,Pengcheng Xu,Jiaqi Li,Ruizhi Pu,Charles Ling,Ian McLeod,Boyu Wang | ICLR 2023,Top 25% | Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting the knowledge from source domain to unlabeled target domain without accessing the private source data. However, existing methods rely on the ... | https://openreview.net/pdf/3132194ea43e68910cd7e90e9be2141425b45f39.pdf | cs.LG cs.CV | 2023-02-27T00:00:00 | when source-free domain adaptation meets learning with noisy labels | 213 | 39 | 213 |
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