Datasets:

DeepNLP
/

ICLR-2023-Accepted-Papers

title stringlengths 18 162	url stringlengths 42 44	detail_url stringlengths 42 44	authors stringlengths 10 429	tags stringclasses 3 values	abstract stringlengths 400 2.37k	pdf stringlengths 71 71
Encoding Recurrence into Transformers	https://openreview.net/forum?id=7YfHla7IxBJ	https://openreview.net/forum?id=7YfHla7IxBJ	Feiqing Huang,Kexin Lu,Yuxi CAI,Zhen Qin,Yanwen Fang,Guangjian Tian,Guodong Li	ICLR 2023,Top 5%	This paper novelly breaks down with ignorable loss an RNN layer into a sequence of simple RNNs, each of which can be further rewritten into a lightweight positional encoding matrix of a self-attention, named the Recurrence Encoding Matrix (REM). Thus, recurrent dynamics introduced by the RNN layer can be encapsulated i...	https://openreview.net/pdf/70636775789b51f219cb29634cc7c794cc86577b.pdf
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
Transfer NAS with Meta-learned Bayesian Surrogates	https://openreview.net/forum?id=paGvsrl4Ntr	https://openreview.net/forum?id=paGvsrl4Ntr	Gresa Shala,Thomas Elsken,Frank Hutter,Josif Grabocka	ICLR 2023,Top 5%	While neural architecture search (NAS) is an intensely-researched area, approaches typically still suffer from either (i) high computational costs or (ii) lack of robustness across datasets and experiments. Furthermore, most methods start searching for an optimal architecture from scratch, ignoring prior knowledge. Thi...	https://openreview.net/pdf/1d6bd2efad6066b8250a1ed96932db04f31c080f.pdf
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
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
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
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
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
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification	https://openreview.net/forum?id=FZdJQgy05rz	https://openreview.net/forum?id=FZdJQgy05rz	Takashi Ishida,Ikko Yamane,Nontawat Charoenphakdee,Gang Niu,Masashi Sugiyama	ICLR 2023,Top 5%	There is a fundamental limitation in the prediction performance that a machine learning model can achieve due to the inevitable uncertainty of the prediction target. In classification problems, this can be characterized by the Bayes error, which is the best achievable error with any classifier. The Bayes error can be u...	https://openreview.net/pdf/adf5cd1db7eb1218ea6e605d13c786cdf71eab45.pdf
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes	https://openreview.net/forum?id=4-k7kUavAj	https://openreview.net/forum?id=4-k7kUavAj	Aviral Kumar,Rishabh Agarwal,Xinyang Geng,George Tucker,Sergey Levine	ICLR 2023,Top 5%	The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP. However, recent works argue that offline RL methods encounter unique challenges to scaling up model ca...	https://openreview.net/pdf/c4fe1442235b5f185dc41908f09f0b65f8faa938.pdf
What learning algorithm is in-context learning? Investigations with linear models	https://openreview.net/forum?id=0g0X4H8yN4I	https://openreview.net/forum?id=0g0X4H8yN4I	Ekin Akyürek,Dale Schuurmans,Jacob Andreas,Tengyu Ma,Denny Zhou	ICLR 2023,Top 5%	Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples $(x, f(x))$ presented in the input without further parameter updates. We investigate the hypothesis that transformer-based in-context learners impl...	https://openreview.net/pdf/7295479b5085774245ad66c73c5176e41b868b67.pdf
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning	https://openreview.net/forum?id=Uuf2q9TfXGA	https://openreview.net/forum?id=Uuf2q9TfXGA	Zeyuan Allen-Zhu,Yuanzhi Li	ICLR 2023,Top 5%	We formally study how \emph{ensemble} of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using \emph{knowledge distillation}. We consider the challenging case where the ensemble is simply an average of the outputs of a few independently t...	https://openreview.net/pdf/fbebb24f15ad18f41fae9b87ca59c93d0a7de7f2.pdf
When and Why Vision-Language Models Behave like Bags-Of-Words, and What to Do About It?	https://openreview.net/forum?id=KRLUvxh8uaX	https://openreview.net/forum?id=KRLUvxh8uaX	Mert Yuksekgonul,Federico Bianchi,Pratyusha Kalluri,Dan Jurafsky,James Zou	ICLR 2023,Top 5%	Despite the success of large vision and language models (VLMs) in many downstream applications, it is unclear how well they encode the compositional relationships between objects and attributes. Here, we create the Attribution, Relation, and Order (ARO) benchmark to systematically evaluate the ability of VLMs to unders...	https://openreview.net/pdf/ced77554985af011f5544a8798a3035d4b6ab52b.pdf
Confidence-Conditioned Value Functions for Offline Reinforcement Learning	https://openreview.net/forum?id=Zeb5mTuqT5	https://openreview.net/forum?id=Zeb5mTuqT5	Joey Hong,Aviral Kumar,Sergey Levine	ICLR 2023,Top 5%	Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offline RL methods must handle distributional shift between the dataset and the learned policy. The most common approach is to learn conservative,...	https://openreview.net/pdf/83d1be96a20a4accfffcc8dd593c0f0a3c5b5776.pdf
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
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection	https://openreview.net/forum?id=H3HcEJA2Um	https://openreview.net/forum?id=H3HcEJA2Um	Jinhyung Park,Chenfeng Xu,Shijia Yang,Kurt Keutzer,Kris M. Kitani,Masayoshi Tomizuka,Wei Zhan	ICLR 2023,Top 5%	While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception. Observing that existing works' fusion of multi-frame images are instances of temporal stereo matching, we find that performance...	https://openreview.net/pdf/1653c1b285d859cb8e3ba8eb36976b1006f2bf1c.pdf
Dichotomy of Control: Separating What You Can Control from What You Cannot	https://openreview.net/forum?id=DEGjDDV22pI	https://openreview.net/forum?id=DEGjDDV22pI	Sherry Yang,Dale Schuurmans,Pieter Abbeel,Ofir Nachum	ICLR 2023,Top 5%	Future- or return-conditioned supervised learning is an emerging paradigm for offline reinforcement learning (RL), in which the future outcome (i.e., return) associated with a sequence of actions in an offline dataset is used as input to a policy trained to imitate those same actions. While return-conditioning is at th...	https://openreview.net/pdf/6570cf14640b106571e1d2ce08ee384f1f17eeaf.pdf
Learning where and when to reason in neuro-symbolic inference	https://openreview.net/forum?id=en9V5F8PR-	https://openreview.net/forum?id=en9V5F8PR-	Cristina Cornelio,Jan Stuehmer,Shell Xu Hu,Timothy Hospedales	ICLR 2023,Top 5%	The integration of hard constraints on neural network outputs is a very desirable capability. This allows to instill trust in AI by guaranteeing the sanity of that neural network predictions with respect to domain knowledge. Recently, this topic has received a lot of attention. However, all the existing methods usually...	https://openreview.net/pdf/31f018dbf1b4f56acf88d2715ebd70a6d3908c99.pdf
On the duality between contrastive and non-contrastive self-supervised learning	https://openreview.net/forum?id=kDEL91Dufpa	https://openreview.net/forum?id=kDEL91Dufpa	Quentin Garrido,Yubei Chen,Adrien Bardes,Laurent Najman,Yann LeCun	ICLR 2023,Top 5%	Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches. While differences between the two families have been thoroughly discussed to motivate new approaches, we focus ...	https://openreview.net/pdf/b65a5392645765469baab2e39bb691bf22a9e6fd.pdf
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
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions	https://openreview.net/forum?id=zyLVMgsZ0U_	https://openreview.net/forum?id=zyLVMgsZ0U_	Sitan Chen,Sinho Chewi,Jerry Li,Yuanzhi Li,Adil Salim,Anru Zhang	ICLR 2023,Top 5%	We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of large-scale real-world generative models such as DALL$\cdot$E 2. Our main result is that, assuming accurate score estimates, such SGMs can eff...	https://openreview.net/pdf/f0dc173be132440952bd7d8221b096d0a0ecf2c7.pdf
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching	https://openreview.net/forum?id=88nT0j5jAn	https://openreview.net/forum?id=88nT0j5jAn	Donggyun Kim,Jinwoo Kim,Seongwoong Cho,Chong Luo,Seunghoon Hong	ICLR 2023,Top 5%	Dense prediction tasks are a fundamental class of problems in computer vision. As supervised methods suffer from high pixel-wise labeling cost, a few-shot learning solution that can learn any dense task from a few labeled images is desired. Yet, current few-shot learning methods target a restricted set of tasks such as...	https://openreview.net/pdf/45149e96f3e88087d3e81a1ff08f0d2b5e719921.pdf
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach	https://openreview.net/forum?id=dLAYGdKTi2	https://openreview.net/forum?id=dLAYGdKTi2	Heshan Devaka Fernando,Han Shen,Miao Liu,Subhajit Chaudhury,Keerthiram Murugesan,Tianyi Chen	ICLR 2023,Top 5%	Many machine learning problems today have multiple objective functions. They appear either in learning with multiple criteria where learning has to make a trade-off between multiple performance metrics such as fairness, safety and accuracy; or, in multi-task learning where multiple tasks are optimized jointly, sharing ...	https://openreview.net/pdf/9e46581e9b775d4b10ffcc00c43e0bdb8d21e1b4.pdf
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
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
Is Conditional Generative Modeling all you need for Decision Making?	https://openreview.net/forum?id=sP1fo2K9DFG	https://openreview.net/forum?id=sP1fo2K9DFG	Anurag Ajay,Yilun Du,Abhi Gupta,Joshua B. Tenenbaum,Tommi S. Jaakkola,Pulkit Agrawal	ICLR 2023,Top 5%	Recent improvements in conditional generative modeling have made it possible to generate high-quality images from language descriptions alone. We investigate whether these methods can directly address the problem of sequential decision-making. We view decision-making not through the lens of reinforcement learning (RL),...	https://openreview.net/pdf/e4e0b6540b8164996a357a85347d96a324cf5647.pdf
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
Agree to Disagree: Diversity through Disagreement for Better Transferability	https://openreview.net/forum?id=K7CbYQbyYhY	https://openreview.net/forum?id=K7CbYQbyYhY	Matteo Pagliardini,Martin Jaggi,François Fleuret,Sai Praneeth Karimireddy	ICLR 2023,Top 5%	Gradient-based learning algorithms have an implicit \emph{simplicity bias} which in effect can limit the diversity of predictors being sampled by the learning procedure. This behavior can hinder the transferability of trained models by (i) favoring the learning of simpler but spurious features --- present in the traini...	https://openreview.net/pdf/bffcee09d1939996b54123724697afa1a9d4df37.pdf
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
Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness	https://openreview.net/forum?id=SMYdcXjJh1q	https://openreview.net/forum?id=SMYdcXjJh1q	Joel Dapello,Kohitij Kar,Martin Schrimpf,Robert Baldwin Geary,Michael Ferguson,David Daniel Cox,James J. DiCarlo	ICLR 2023,Top 5%	While some state-of-the-art artificial neural network systems in computer vision are strikingly accurate models of the corresponding primate visual processing, there are still many discrepancies between these models and the behavior of primates on object recognition tasks. Many current models suffer from extreme sensit...	https://openreview.net/pdf/9c4c1940dba43cb5ad6502b7a23339d19d3a9a49.pdf
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
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
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning	https://openreview.net/forum?id=3Pf3Wg6o-A4	https://openreview.net/forum?id=3Pf3Wg6o-A4	Antonia Creswell,Murray Shanahan,Irina Higgins	ICLR 2023,Top 5%	Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reasoning problems. Here we carry out a comprehensive evaluation of LLMs on 46 tasks that probe different aspects of logical reasoning. We show ...	https://openreview.net/pdf/4c8f591f9bb58ccd07ed826e0e57885bc4227b12.pdf
Compressing multidimensional weather and climate data into neural networks	https://openreview.net/forum?id=Y5SEe3dfniJ	https://openreview.net/forum?id=Y5SEe3dfniJ	Langwen Huang,Torsten Hoefler	ICLR 2023,Top 5%	Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the dat...	https://openreview.net/pdf/6959d1573e13008d77bafdde3a013ed0767d1185.pdf
Confidential-PROFITT: Confidential PROof of FaIr Training of Trees	https://openreview.net/forum?id=iIfDQVyuFD	https://openreview.net/forum?id=iIfDQVyuFD	Ali Shahin Shamsabadi,Sierra Calanda Wyllie,Nicholas Franzese,Natalie Dullerud,Sébastien Gambs,Nicolas Papernot,Xiao Wang,Adrian Weller	ICLR 2023,Top 5%	Post hoc auditing of model fairness suffers from potential drawbacks: (1) auditing may be highly sensitive to the test samples chosen; (2) the model and/or its training data may need to be shared with an auditor thereby breaking confidentiality. We address these issues by instead providing a certificate that demonstrat...	https://openreview.net/pdf/20b12822064b2d7eb054021a0f1209e1dd066515.pdf
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
Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives	https://openreview.net/forum?id=0Ij9_q567Ma	https://openreview.net/forum?id=0Ij9_q567Ma	Shaokun Zhang,Feiran Jia,Chi Wang,Qingyun Wu	ICLR 2023,Top 5%	Motivated by various practical applications, we propose a novel and general formulation of targeted multi-objective hyperparameter optimization. Our formulation allows a clear specification of an automatable optimization goal using lexicographic preference over multiple objectives. We then propose a randomized directed...	https://openreview.net/pdf/01544b5bcb68c0bc76fbffa2876dca9d12ec0f24.pdf
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning	https://openreview.net/forum?id=F61FwJTZhb	https://openreview.net/forum?id=F61FwJTZhb	Anton Bakhtin,David J Wu,Adam Lerer,Jonathan Gray,Athul Paul Jacob,Gabriele Farina,Alexander H Miller,Noam Brown	ICLR 2023,Top 5%	No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is insufficient for achiev...	https://openreview.net/pdf/5355b9a9bc1eabd198a78654d7dbfa4e5f1664b0.pdf
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
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
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
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
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
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
Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics	https://openreview.net/forum?id=vmjctNUSWI	https://openreview.net/forum?id=vmjctNUSWI	Kuo-Hao Zeng,Luca Weihs,Roozbeh Mottaghi,Ali Farhadi	ICLR 2023,Top 5%	A common assumption when training embodied agents is that the impact of taking an action is stable; for instance, executing the ``move ahead'' action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise. This assumption is limiting; an agent may encounter sett...	https://openreview.net/pdf/5fd307801a722f24990855f8235ae461cabf66fa.pdf
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
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
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier	https://openreview.net/forum?id=OpC-9aBBVJe	https://openreview.net/forum?id=OpC-9aBBVJe	Pierluca D'Oro,Max Schwarzer,Evgenii Nikishin,Pierre-Luc Bacon,Marc G Bellemare,Aaron Courville	ICLR 2023,Top 5%	Increasing the replay ratio, the number of updates of an agent's parameters per environment interaction, is an appealing strategy for improving the sample efficiency of deep reinforcement learning algorithms. In this work, we show that fully or partially resetting the parameters of deep reinforcement learning agents ca...	https://openreview.net/pdf/c891095f8e46b891138ef064f19d6b0e2d84dcb2.pdf
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness	https://openreview.net/forum?id=Wc5bmZZU9cy	https://openreview.net/forum?id=Wc5bmZZU9cy	Shuaichen Chang,Jun Wang,Mingwen Dong,Lin Pan,Henghui Zhu,Alexander Hanbo Li,Wuwei Lan,Sheng Zhang,Jiarong Jiang,Joseph Lilien,Steve Ash,William Yang Wang,Zhiguo Wang,Vittorio Castelli,Patrick Ng,Bing Xiang	ICLR 2023,Top 5%	Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models are vulnerable to task-specific perturbations. Previous curated robustness test sets usually focus on individual phenomena. In this paper, we ...	https://openreview.net/pdf/28dd8eb27d485f652c4874af1d995452557ae2b3.pdf
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
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs	https://openreview.net/forum?id=SMa9EAovKMC	https://openreview.net/forum?id=SMa9EAovKMC	Albert Qiaochu Jiang,Sean Welleck,Jin Peng Zhou,Timothee Lacroix,Jiacheng Liu,Wenda Li,Mateja Jamnik,Guillaume Lample,Yuhuai Wu	ICLR 2023,Top 5%	The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains arduous and only accessible to a few experts. While previous studies to automate formalization focused on powerful search algorithms, no att...	https://openreview.net/pdf/cfd03f19d20263d9c1d1cc026a2b3528392fc857.pdf
REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH	https://openreview.net/forum?id=uVcDssQff_	https://openreview.net/forum?id=uVcDssQff_	Duc N.M Hoang,Shiwei Liu,Radu Marculescu,Zhangyang Wang	ICLR 2023,Top 5%	Pruning neural networks at initialization (PaI) has received an upsurge of interest due to its end-to-end saving potential. PaI is able to find sparse subnetworks at initialization that can achieve comparable performance to the full networks. These methods can surpass the trivial baseline of random pruning but suffer f...	https://openreview.net/pdf/f73064906e38441e21dd0a622065469ef3f5b5bd.pdf
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
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification	https://openreview.net/forum?id=YnkGMIh0gvX	https://openreview.net/forum?id=YnkGMIh0gvX	Paul F Jaeger,Carsten Tim Lüth,Lukas Klein,Till J. Bungert	ICLR 2023,Top 5%	Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established approaches aims to detect erroneous predictions by means of assigning confidence scores. This confidence may be obtained by either quantifying th...	https://openreview.net/pdf/a5de8999d6fc1e463fee479f14b17ae999f6cbc2.pdf
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search	https://openreview.net/forum?id=7JsGYvjE88d	https://openreview.net/forum?id=7JsGYvjE88d	Michał Zawalski,Michał Tyrolski,Konrad Czechowski,Tomasz Odrzygóźdź,Damian Stachura,Piotr Piękos,Yuhuai Wu,Łukasz Kuciński,Piotr Miłoś	ICLR 2023,Top 5%	Complex reasoning problems contain states that vary in the computational cost required to determine the right action plan. To take advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoa...	https://openreview.net/pdf/361fb386c64c303b0467dd1fb8d3946766d58d4c.pdf
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
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
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
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation	https://openreview.net/forum?id=M95oDwJXayG	https://openreview.net/forum?id=M95oDwJXayG	Marius-Constantin Dinu,Markus Holzleitner,Maximilian Beck,Hoan Duc Nguyen,Andrea Huber,Hamid Eghbal-zadeh,Bernhard A. Moser,Sergei Pereverzyev,Sepp Hochreiter,Werner Zellinger	ICLR 2023,Top 5%	We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, drawn from a different input distribution. We follow the strategy to compute several models using different hyper-parameters, and, to subsequen...	https://openreview.net/pdf/36c115dd350b35beffcf18cbfd0a6afd2ab5a0e7.pdf
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search	https://openreview.net/forum?id=ZTK3SefE8_Z	https://openreview.net/forum?id=ZTK3SefE8_Z	Fangzheng Sun,Yang Liu,Jian-Xun Wang,Hao Sun	ICLR 2023,Top 5%	Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challenging. To tackle this fundamental issue, we propose a novel Symbolic Physics Learner (SPL) machine to di...	https://openreview.net/pdf/0c815f206ac64432f9caf1f36b816f9e368dee15.pdf
Clean-image Backdoor: Attacking Multi-label Models with Poisoned Labels Only	https://openreview.net/forum?id=rFQfjDC9Mt	https://openreview.net/forum?id=rFQfjDC9Mt	Kangjie Chen,Xiaoxuan Lou,Guowen Xu,Jiwei Li,Tianwei Zhang	ICLR 2023,Top 5%	Multi-label models have been widely used in various applications including image annotation and object detection. The fly in the ointment is its inherent vulnerability to backdoor attacks due to the adoption of deep learning techniques. However, all existing backdoor attacks exclusively require to modify training input...	https://openreview.net/pdf/6021cbdfd717a31730914f92bc2b1e9762135b65.pdf
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
Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction	https://openreview.net/forum?id=_2bDpAtr7PI	https://openreview.net/forum?id=_2bDpAtr7PI	David Klee,Ondrej Biza,Robert Platt,Robin Walters	ICLR 2023,Top 5%	Predicting the pose of objects from a single image is an important but difficult computer vision problem. Methods that predict a single point estimate do not predict the pose of objects with symmetries well and cannot represent uncertainty. Alternatively, some works predict a distribution over orientations in $\mathrm{...	https://openreview.net/pdf/dc2578c49b3cfc78beece0602f3564947a512c18.pdf
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting	https://openreview.net/forum?id=zt53IDUR1U	https://openreview.net/forum?id=zt53IDUR1U	Huiqiang Wang,Jian Peng,Feihu Huang,Jince Wang,Junhui Chen,Yifei Xiao	ICLR 2023,Top 5%	Recently, Transformer-based methods have achieved surprising performance in the field of long-term series forecasting, but the attention mechanism for computing global correlations entails high complexity. And they do not allow for targeted modeling of local features as CNN structures do. To solve the above problems, w...	https://openreview.net/pdf/6e3044ae6e9494f027b7c011f97efa8f0ed029c0.pdf
Personalized Federated Learning with Feature Alignment and Classifier Collaboration	https://openreview.net/forum?id=SXZr8aDKia	https://openreview.net/forum?id=SXZr8aDKia	Jian Xu,Xinyi Tong,Shao-Lun Huang	ICLR 2023,Top 5%	Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One such approach in deep neural networks based tasks is employing a shared feature representation and learning a customized classifier head for...	https://openreview.net/pdf/7e45d7414cae758349f97df5277f8897ef7b8c04.pdf
From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data	https://openreview.net/forum?id=c7rM7F7jQjN	https://openreview.net/forum?id=c7rM7F7jQjN	Zichen Jeff Cui,Yibin Wang,Nur Muhammad Mahi Shafiullah,Lerrel Pinto	ICLR 2023,Top 5%	While large-scale sequence modelling from offline data has led to impressive performance gains in natural language generation and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from no...	https://openreview.net/pdf/2ac61e4b87940fa144ced394ae19abce9e89a184.pdf
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
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation	https://openreview.net/forum?id=w0QXrZ3N-s	https://openreview.net/forum?id=w0QXrZ3N-s	Zihui Xue,Zhengqi Gao,Sucheng Ren,Hang Zhao	ICLR 2023,Top 5%	Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achieve knowledge transfer across modalities, a pretrained network from one modality is adopted as the teacher to provide supervision signals to...	https://openreview.net/pdf/741eead42fe714d67fac001285243a76fd4ad259.pdf
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
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
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
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task	https://openreview.net/forum?id=DeG07_TcZvT	https://openreview.net/forum?id=DeG07_TcZvT	Kenneth Li,Aspen K Hopkins,David Bau,Fernanda Viégas,Hanspeter Pfister,Martin Wattenberg	ICLR 2023,Top 5%	Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this question by applying ...	https://openreview.net/pdf/70fb51a26cffdf3304e24f4d2e803b729904fe20.pdf
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
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
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
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
AutoGT: Automated Graph Transformer Architecture Search	https://openreview.net/forum?id=GcM7qfl5zY	https://openreview.net/forum?id=GcM7qfl5zY	Zizhao Zhang,Xin Wang,Chaoyu Guan,Ziwei Zhang,Haoyang Li,Wenwu Zhu	ICLR 2023,Top 5%	Although Transformer architectures have been successfully applied to graph data with the advent of Graph Transformer, current design of Graph Transformer still heavily relies on human labor and expertise knowledge to decide proper neural architectures and suitable graph encoding strategies at each Transformer layer. In...	https://openreview.net/pdf/ea1ae3473367dc3011d3f2b84c2b2192c39aee04.pdf
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting	https://openreview.net/forum?id=vSVLM2j9eie	https://openreview.net/forum?id=vSVLM2j9eie	Yunhao Zhang,Junchi Yan	ICLR 2023,Top 5%	Recently many deep models have been proposed for multivariate time series (MTS) forecasting. In particular, Transformer-based models have shown great potential because they can capture long-term dependency. However, existing Transformer-based models mainly focus on modeling the temporal dependency (cross-time dependenc...	https://openreview.net/pdf/1d793d6ba7c00ecfe98128614d58e2493255bd89.pdf
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
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization	https://openreview.net/forum?id=ueYYgo2pSSU	https://openreview.net/forum?id=ueYYgo2pSSU	Haoran Xu,Li Jiang,Jianxiong Li,Zhuoran Yang,Zhaoran Wang,Victor Wai Kin Chan,Xianyuan Zhan	ICLR 2023,Top 5%	Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the deviation from the behavior policy as computing $Q$-values using out-of-distribution (OOD) actions will suffer from errors due to distributional...	https://openreview.net/pdf/dbd2c001478b511324bdbec3a393c6f1552fbb3d.pdf
Win: Weight-Decay-Integrated Nesterov Acceleration for Adaptive Gradient Algorithms	https://openreview.net/forum?id=CPdc77SQfQ5	https://openreview.net/forum?id=CPdc77SQfQ5	Pan Zhou,Xingyu Xie,Shuicheng YAN	ICLR 2023,Top 5%	Training deep networks on large-scale datasets is computationally challenging. In this work, we explore the problem of ``\textit{how to accelerate adaptive gradient algorithms in a general manner}", and aim to provide practical efficiency-boosting insights. To this end, we propose an effective and general {W...	https://openreview.net/pdf/b3453f304fc9650f5fcaa04d42bafe01e1c5bd1a.pdf
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
MocoSFL: enabling cross-client collaborative self-supervised learning	https://openreview.net/forum?id=2QGJXyMNoPz	https://openreview.net/forum?id=2QGJXyMNoPz	Jingtao Li,Lingjuan Lyu,Daisuke Iso,Chaitali Chakrabarti,Michael Spranger	ICLR 2023,Top 5%	Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Federated Learning (SFL) and Momentum Contrast ...	https://openreview.net/pdf/e7d98a4942f9fa3e0236bec53218b97e0792f3ee.pdf
DaxBench: Benchmarking Deformable Object Manipulation with Differentiable Physics	https://openreview.net/forum?id=1NAzMofMnWl	https://openreview.net/forum?id=1NAzMofMnWl	Siwei Chen,Yiqing Xu,Cunjun Yu,Linfeng Li,Xiao Ma,Zhongwen Xu,David Hsu	ICLR 2023,Top 5%	Deformable object manipulation (DOM) is a long-standing challenge in robotics and has attracted significant interest recently. This paper presents DaXBench, a differentiable simulation framework for DOM. While existing work often focuses on a specific type of deformable objects, DaXBench supports fluid, rope, cloth ......	https://openreview.net/pdf/3c5184bef72b67b8b06885038e921049f56dc94e.pdf
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
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
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
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
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection	https://openreview.net/forum?id=FeWvD0L_a4	https://openreview.net/forum?id=FeWvD0L_a4	Jiajun Fan,Yuzheng Zhuang,Yuecheng Liu,Jianye HAO,Bin Wang,Jiangcheng Zhu,Hao Wang,Shu-Tao Xia	ICLR 2023,Top 5%	The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with population-based methods, which collect samples with diverse behaviors derived from a population of different exploratory policies. Adaptive policy selection has been adopte...	https://openreview.net/pdf/6576875018fe482d865d62a571a8b8df3278b360.pdf
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
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
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
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
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering	https://openreview.net/forum?id=688hNNMigVX	https://openreview.net/forum?id=688hNNMigVX	Liyao Li,Haobo Wang,Liangyu Zha,Qingyi Huang,Sai Wu,Gang Chen,Junbo Zhao	ICLR 2023,Top 25%	Feature engineering is widely acknowledged to be pivotal in tabular data analysis and prediction. Automated feature engineering (AutoFE) emerged to automate this process managed by experienced data scientists and engineers conventionally. In this area, most — if not all — prior work adopted an identical framework from ...	https://openreview.net/pdf/1c15c68dc3b8354cfb9326758f23b4ffaddbca2d.pdf
Learning Group Importance using the Differentiable Hypergeometric Distribution	https://openreview.net/forum?id=75O7S_L4oY	https://openreview.net/forum?id=75O7S_L4oY	Thomas M. Sutter,Laura Manduchi,Alain Ryser,Julia E Vogt	ICLR 2023,Top 25%	Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned - be it the cluster sizes in clustering applications or the number of shared versus independent generative latent factors in weakly-supervised learning. Probability d...	https://openreview.net/pdf/eaa362d272c28b62b383ba46f668c0058f49115c.pdf
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
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery	https://openreview.net/forum?id=6BHlZgyPOZY	https://openreview.net/forum?id=6BHlZgyPOZY	Felix Chalumeau,Raphael Boige,Bryan Lim,Valentin Macé,Maxime Allard,Arthur Flajolet,Antoine Cully,Thomas PIERROT	ICLR 2023,Top 25%	Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for training neural policies to solve complex control tasks. However, these policies tend to be overfit to the exact specifications of the task and environment they were trained on, and thus do not perform well when conditions deviate slightly or when ...	https://openreview.net/pdf/1c63093c2dc46ae51a5d9ec802a0d85f3455069d.pdf
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
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
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

End of preview. Expand in Data Studio

ICLR 2023 International Conference on Learning Representations 2023 Accepted Paper Meta Info Dataset

This dataset is collect from the ICLR 2023 OpenReview website (https://openreview.net/group?id=ICLR.cc/2023/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iclr2023). For researchers who are interested in doing analysis of ICLR 2023 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the ICLR 2023 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.

Meta Information of Json File

{
    "title": "Encoding Recurrence into Transformers",
    "url": "https://openreview.net/forum?id=7YfHla7IxBJ",
    "detail_url": "https://openreview.net/forum?id=7YfHla7IxBJ",
    "authors": "Feiqing Huang,Kexin Lu,Yuxi CAI,Zhen Qin,Yanwen Fang,Guangjian Tian,Guodong Li",
    "tags": "ICLR 2023,Top 5%",
    "abstract": "This paper novelly breaks down with ignorable loss an RNN layer into a sequence of simple RNNs, each of which can be further rewritten into a lightweight positional encoding matrix of a self-attention, named the Recurrence Encoding Matrix (REM). Thus, recurrent dynamics introduced by the RNN layer can be encapsulated into the positional encodings of a multihead self-attention, and this makes it possible to seamlessly incorporate these recurrent dynamics into a Transformer, leading to a new module, Self-Attention with Recurrence (RSA). The proposed module can leverage the recurrent inductive bias of REMs to achieve a better sample efficiency than its corresponding baseline Transformer, while the self-attention is used to model the remaining non-recurrent signals. The relative proportions of these two components are controlled by a data-driven gated mechanism, and the effectiveness of RSA modules are demonstrated by four sequential learning tasks.",
    "pdf": "https://openreview.net/pdf/70636775789b51f219cb29634cc7c794cc86577b.pdf"
}

AI Equation

List of AI Equations and Latex
List of Math Equations and Latex
List of Physics Equations and Latex
List of Statistics Equations and Latex
List of Machine Learning Equations and Latex

AI Agent Marketplace and Search

AI Agent Marketplace and Search
Robot Search
Equation and Academic search
AI & Robot Comprehensive Search
AI & Robot Question
AI & Robot Community
AI Agent Marketplace Blog

AI Agent Reviews

AI Agent Marketplace Directory
Microsoft AI Agents Reviews
Claude AI Agents Reviews
OpenAI AI Agents Reviews
Saleforce AI Agents Reviews
AI Agent Builder Reviews

Downloads last month: 229

ICLR 2023 International Conference on Learning Representations 2023 Accepted Paper Meta Info Dataset

Meta Information of Json File

Related

AI Equation

AI Agent Marketplace and Search

AI Agent Reviews