Neural Information Processing Systems Foundation, Inc. (NeurIPS)

Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Efficient Recipe for Long Context Extension via Middle-Focused Positional Encoding

Tong Wu, Yanpeng Zhao, Zilong Zheng p56349-56373 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

Xiu-Shen Wei, H.-Y. Xu, Faen Zhang, Yuxin Peng, Wei Zhou p14489-14500 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Empirical Study on Disentanglement of Negative-Free Contrastive Learning

Jinkun Cao, Ruiqian Nai, Qing Yang, Jialei Huang, Yang Gao p1210-1222 from Advances in Neural Information Processing Systems 35

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An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations

Haoran Yang, Xiangyu Zhao, Yicong Li, Hongxu Chen, Guandong Xu p62853-62868 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval

Huilong Jin, Yingxue Zhang, Lei Shi, Shuang Zhang, Feifei Kou, Jiapeng Yang, Chuangying Zhu, Jia Luo p2106-2126 from Advances in Neural Information Processing Systems 37

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An engine not a camera: Measuring performative power of online search

Celestine Mendler-Dünner, Gabriele Carovano, Moritz Hardt p59266-59288 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Equivalence Between Static and Dynamic Regret Minimization

Andrew Jacobsen, Francesco Orabona p98835-98870 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem

Yoonsoo Nam, Nayara Fonseca, Seok Lee, Chris Mingard, Ard Louis p39632-39693 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

Weimin Bai, Yifei Wang, Wenzheng Chen, He Sun p19447-19471 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits

Shinji Ito, Kei Takemura p71582-71602 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An eye for an ear: zero-shot audio description leveraging an image captioner with audio-visual token distribution matching

Hugo Malard, Michel Olvera, Stéphane Lathuiliere, Slim Essid p38720-38743 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Image is Worth 32 Tokens for Reconstruction and Generation

Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen p128940-128966 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Improved Empirical Fisher Approximation for Natural Gradient Descent

Xiaodong Wu, Wenyi Yu, Chao Zhang, Philip Woodland p134151-134194 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits

Kiarash Banihashem, Mohammadtaghi Hajiaghayi, Suho Shin, Max Springer p67471-67486 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models

Yunzhe Hu, Difan Zou, Dong Xu p116815-116837 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An In-Depth Study of Stochastic Backpropagation

Jun Fang, Mingze Xu, Hao Chen, Bing Shuai, Zhuowen Tu, Joseph Tighe p21819-21830 from Advances in Neural Information Processing Systems 35

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An Inductive Bias for Tabular Deep Learning

Ege Beyazit, Jonathan Kozaczuk, Bo Li, Vanessa Wallace, Bilal Fadlallah p43108-43135 from Advances in Neural Information Processing Systems 36

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An Information Theoretic Perspective on Conformal Prediction

Alvaro Correia, Fabio Massoli, Christos Louizos, Arash Behboodi p101000-101041 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Information Theory Perspective on Variance-Invariance-Covariance Regularization

Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann Lecun p33965-33998 from Advances in Neural Information Processing Systems 36

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An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions

Mohammad Jalali, Cheuk Ting Li, Farzan Farnia p9931-9943 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Information-Theoretic Framework for Deep Learning

Hong Jun Jeon, Benjamin Van Roy p3279-3291 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An information-theoretic quantification of the content of communication between brain regions

Marco Celotto, Jan Bím, Alejandro Tlaie, Vito De Feo, Alessandro Toso, Stefan Lemke, Daniel Chicharro, Hamed Nili, Malte Bieler, Ileana Hanganu-Opatz, Tobias Donner, Andrea Brovelli, Stefano Panzeri p64213-64265 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Inverse Scaling Law for CLIP Training

Xianhang Li, Zeyu Wang, Cihang Xie p49068-49087 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Investigation into Whitening Loss for Self-Supervised Learning

Xi Weng, Lei Huang, Lei Zhao, Rao Anwer, Salman Khan, Fahad Shahbaz Khan p29748-29760 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

An Efficient Recipe for Long Context Extension via Middle-Focused Positional Encoding

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

An Empirical Study on Disentanglement of Negative-Free Contrastive Learning

An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations

An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval

An engine not a camera: Measuring performative power of online search

An Equivalence Between Static and Dynamic Regret Minimization

An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem

An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits

An eye for an ear: zero-shot audio description leveraging an image captioner with audio-visual token distribution matching

An Image is Worth 32 Tokens for Reconstruction and Generation

An Improved Empirical Fisher Approximation for Natural Gradient Descent

An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models

An In-Depth Study of Stochastic Backpropagation

An Inductive Bias for Tabular Deep Learning

An Information Theoretic Perspective on Conformal Prediction

An Information Theory Perspective on Variance-Invariance-Covariance Regularization

An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions

An Information-Theoretic Framework for Deep Learning

An information-theoretic quantification of the content of communication between brain regions

An Inverse Scaling Law for CLIP Training

An Investigation into Whitening Loss for Self-Supervised Learning

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