NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 3 items • Updated
title stringlengths 14 150 | paper_url stringlengths 42 42 | authors listlengths 1 28 | type stringclasses 3
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values | abstract large_stringlengths 386 2.56k | keywords listlengths 1 18 | TL;DR large_stringlengths 0 250 ⌀ | submission_number int64 8 15.6k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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Online PCA in Converging Self-consistent Field Equations | https://openreview.net/forum?id=vq11gurmUY | [
"Xihan Li",
"Xiang Chen",
"Rasul Tutunov",
"Haitham Bou Ammar",
"Lei Wang",
"Jun Wang"
] | Poster | null | Self-consistent Field (SCF) equation is a type of nonlinear eigenvalue problem in which the matrix to be eigen-decomposed is a function of its own eigenvectors. It is of great significance in computational science for its connection to the Schrödinger equation. Traditional fixed-point iteration methods for solving such... | [
"Self-consistent Field Equation",
"Computational Science",
"Online PCA"
] | We developed a new algorithm based on online PCA to converge Self-consistent Field Equations | 15,599 | null | null | [
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Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy | https://openreview.net/forum?id=zyZkaqNnpa | [
"Aahlad Manas Puli",
"Lily H Zhang",
"Yoav Wald",
"Rajesh Ranganath"
] | Poster | null | Common explanations for shortcut learning assume that the shortcut improves prediction only under the training distribution. Thus, models trained in the typical way by minimizing log-loss using gradient descent, which we call default-ERM, should utilize the shortcut. However, even when the stable feature determines the... | [
"shortcut learning",
"spurious correlations",
"perfect stable feature",
"perception tasks",
"implicit bias in optimization",
"improving inductive biases"
] | Implicit biases toward maximizing margins induce shortcut learning in ERM even in tasks with perfect stable features, controlling margins mitigates shortcuts | 15,594 | 2308.12553 | title_snapshot | [
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On Slicing Optimality for Mutual Information | https://openreview.net/forum?id=JMuKfZx2xU | [
"Ammar Fayad",
"Majd Ibrahim"
] | Poster | null | Measuring dependence between two random variables is of great importance in various domains but is difficult to compute in today's complex environments with high-dimensional data. Recently, slicing methods have shown to be a scalable approach to measuring mutual information (MI) between high-dimensional variables by pr... | [
"Mutual information",
"Information Theory"
] | null | 15,586 | null | null | [
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k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy | https://openreview.net/forum?id=9zV2OXCrVF | [
"Chenglin Fan",
"Ping Li",
"Xiaoyun Li"
] | Poster | null | In clustering algorithms, the choice of initial centers is crucial for the quality of the learned clusters. We propose a new initialization scheme for the $k$-median problem in the general metric space (e.g., discrete space induced by graphs), based on the construction of metric embedding tree structure of the data. We... | [
"privacy",
"clustering"
] | null | 15,580 | 2206.12895 | title_snapshot | [
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Information Maximization Perspective of Orthogonal Matching Pursuit with Applications to Explainable AI | https://openreview.net/forum?id=CAF4CnUblx | [
"Aditya Chattopadhyay",
"Ryan Pilgrim",
"Rene Vidal"
] | Spotlight | null | Information Pursuit (IP) is a classical active testing algorithm for predicting an output by sequentially and greedily querying the input in order of information gain. However, IP is computationally intensive since it involves estimating mutual information in high-dimensional spaces. This paper explores Orthogonal Matc... | [
"Information Maximization",
"Sparse Coding",
"Orthogonal Matching Pursuit",
"Explainable AI",
"Information Pursuit"
] | We show that the popular OMP algorithm can be derived from information-theoretic principles modulo a normalization factor. We then use this insight to design a computationally simple sparse-coding based explainable AI algorithm. | 15,576 | null | null | [
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STEVE-1: A Generative Model for Text-to-Behavior in Minecraft | https://openreview.net/forum?id=YkBDJWerKg | [
"Shalev Lifshitz",
"Keiran Paster",
"Harris Chan",
"Jimmy Ba",
"Sheila A. McIlraith"
] | Spotlight | null | Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces a methodology, inspired by unCLIP, for instruction-tuning generative models of behavior without relying on a large dataset of instruction-labeled trajectories. Using this method... | [
"minecraft",
"instruction following",
"foundation models",
"sequence models",
"reinforcement learning",
"sequential decision making",
"goal conditioned reinforcement learning",
"text conditioned reinforcement learning",
"transformers",
"deep learning"
] | We introduce a methodology for instruction-tuning generative models of behavior without relying on a large dataset of instruction-labeled trajectories. | 15,575 | 2306.00937 | title_snapshot | [
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AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity | https://openreview.net/forum?id=7ntI4kcoqG | [
"Jingyuan Li",
"Leo Scholl",
"Trung Le",
"Pavithra Rajeswaran",
"Amy L Orsborn",
"Eli Shlizerman"
] | Poster | null | Latent Variable Models (LVMs) propose to model the dynamics of neural populations by capturing low-dimensional structures that represent features involved in neural activity. Recent LVMs are based on deep learning methodology where a deep neural network is trained to reconstruct the same neural activity given as input ... | [
"Neuroscience and Cognitive Science",
"Neural Activity Forecasting",
"Graph Neural Network"
] | In this work, we emphasize the importance of modeling neural population dynamics via forecasting tasks and aim to improve the forecasting performance with Graph Neural Networks | 15,562 | null | null | [
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Conditional Matrix Flows for Gaussian Graphical Models | https://openreview.net/forum?id=GYnbubCXhE | [
"Marcello Massimo Negri",
"Fabricio Arend Torres",
"Volker Roth"
] | Poster | null | Studying conditional independence among many variables with few observations is a challenging task.
Gaussian Graphical Models (GGMs) tackle this problem by encouraging sparsity in the precision matrix through $l_q$ regularization with $q\leq1$.
However, most GMMs rely on the $l_1$ norm because the objective is highly n... | [
"normalizing flow",
"variational inference",
"graphical lasso",
"gaussian graphical model",
"bayesian inference"
] | General framework for variational inference with matrix-variate Normalizing Flow in Gaussian Graphical Models | 15,533 | 2306.07255 | title_snapshot | [
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Representational Strengths and Limitations of Transformers | https://openreview.net/forum?id=36DxONZ9bA | [
"Clayton Sanford",
"Daniel Hsu",
"Matus Telgarsky"
] | Poster | null | Attention layers, as commonly used in transformers, form the backbone of modern deep learning, yet there is no mathematical description of their benefits and deficiencies as compared with other architectures. In this work we establish both positive and negative results on the representation power of attention layers, w... | [
"self-attention",
"approximation theory",
"communication complexity"
] | We give function approximation tasks that demonstrate the advantages of transformers over RNNs and FNNs, the impact of the self-attention embedding dimension on expressivity, and the limitations of any bounded-size layer of self-attention. | 15,514 | 2306.02896 | title_snapshot | [
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... |
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer | https://openreview.net/forum?id=Srt1hhQgqa | [
"Bowen Tan",
"Yun Zhu",
"Lijuan Liu",
"Eric Xing",
"Zhiting Hu",
"Jindong Chen"
] | Poster | null | Large language models (LLMs) such as T0, FLAN, and OPT-IML excel in multi-tasking under a unified instruction-following paradigm, where they also exhibit remarkable generalization abilities to unseen tasks. Despite their impressive performance, these LLMs, with sizes ranging from several billion to hundreds of billions... | [
"multi-task",
"large language models",
"pretrain model"
] | we introduce a pretrained small scorer, Cappy, designed to enhance the performance and efficiency of multi-task LLMs. | 15,503 | 2311.06720 | title_snapshot | [
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... |
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