Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

    Conglong Li, Minjia Zhang, Yuxiong He p26736-26750 from Advances in Neural Information Processing Systems 35
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  • The Star Geometry of Critic-Based Regularizer Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Star Geometry of Critic-Based Regularizer Learning

    Oscar Leong, Eliza O'Reilly, Yong Soh p71240-71276 from Advances in Neural Information Processing Systems 37
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  • The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track

    Eshta Bhardwaj, Harshit Gujral, Siyi Wu, Ciara Zogheib, Tegan Maharaj, Christoph Becker p53626-53648 from Advances in Neural Information Processing Systems 37
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  • The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation

    Saurabh Saxena, Charles Herrmann, Junhwa Hur, Abhishek Kar, Mohammad Norouzi, Deqing Sun, David Fleet p39443-39469 from Advances in Neural Information Processing Systems 36
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  • The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

    Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, Yi Wu p24611-24624 from Advances in Neural Information Processing Systems 35
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  • The Surprising Effectiveness of SP Voting with Partial Preferences

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Surprising Effectiveness of SP Voting with Partial Preferences

    Hadi Hosseini, Debmalya Mandal, Amrit Puhan p2787-2829 from Advances in Neural Information Processing Systems 37
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  • The surprising efficiency of temporal difference learning for rare event prediction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The surprising efficiency of temporal difference learning for rare event prediction

    Xiaoou Cheng, Jonathan Weare p81257-81286 from Advances in Neural Information Processing Systems 37
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  • The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning

    Moritz Schneider, Robert Krug, Narunas Vaskevicius, Luigi Palmieri, Joschka Boedecker p32916-32946 from Advances in Neural Information Processing Systems 37
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  • The Target-Charging Technique for Privacy Analysis across Interactive Computations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Target-Charging Technique for Privacy Analysis across Interactive Computations

    Edith Cohen, Xin Lyu p62139-62168 from Advances in Neural Information Processing Systems 36
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  • The ToMCAT Dataset

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The ToMCAT Dataset

    Adarsh Pyarelal, Eric Duong, Caleb Shibu, Paulo Soares, Savannah Boyd, Payal Khosla, Valeria A. Pfeifer, Diheng Zhang, Eric Andrews, Rick Champlin, Vincent Raymond, Meghavarshini Krishnaswamy, Clayton Morrison, Emily Butler, Kobus Barnard p40872-40923 from Advances in Neural Information Processing Systems 36
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  • The Trade-Offs of Model Size in Large Recommendation Models : 100GB to 10MB Criteo-Tb DLRM Model

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Trade-Offs of Model Size in Large Recommendation Models : 100GB to 10MB Criteo-Tb DLRM Model

    Aditya Desai, Anshumali Shrivastava p33961-33972 from Advances in Neural Information Processing Systems 35
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  • The Transient Nature of Emergent In-Context Learning in Transformers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Transient Nature of Emergent In-Context Learning in Transformers

    Aaditya Singh, Stephanie Chan, Ted Moskovitz, Erin Grant, Andrew Saxe, Felix Hill p27801-27819 from Advances in Neural Information Processing Systems 36
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  • The tree autoencoder model, with application to hierarchical data visualization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The tree autoencoder model, with application to hierarchical data visualization

    Miguel Carreira-Perpiñán, Kuat Gazizov p32281-32306 from Advances in Neural Information Processing Systems 37
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  • The Tunnel Effect: Building Data Representations in Deep Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Tunnel Effect: Building Data Representations in Deep Neural Networks

    Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani, Razvan Pascanu, Piotr Milos, Tomasz Trzcinski p76772-76805 from Advances in Neural Information Processing Systems 36
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  • The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better

    Scott Geng, Cheng-Yu Hsieh, Vivek Ramanujan, Matthew Wallingford, Chun-Liang Li, Pang Koh, Ranjay Krishna p7902-7929 from Advances in Neural Information Processing Systems 37
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  • The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

    Peter Kocsis, Peter Súkeník, Guillem Braso, Matthias Niessner, Laura Leal-Taixé, Ismail Elezi p1896-1908 from Advances in Neural Information Processing Systems 35
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  • The Unreliability of Explanations in Few-Shot Prompting for Textual Reasoning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Unreliability of Explanations in Few-Shot Prompting for Textual Reasoning

    Xi Ye, Greg Durrett p30378-30392 from Advances in Neural Information Processing Systems 35
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  • The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes

    Eoin Kenny, Weipeng Huang p52907-52935 from Advances in Neural Information Processing Systems 36
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  • The Value of Reward Lookahead in Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Value of Reward Lookahead in Reinforcement Learning

    Nadav Merlis, Dorian Baudry, Vianney Perchet p83627-83664 from Advances in Neural Information Processing Systems 37
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  • The Waymo Open Sim Agents Challenge

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Waymo Open Sim Agents Challenge

    Nico Montali, John Lambert, Paul Mougin, Alex Kuefler, Nicholas Rhinehart, Michelle Li, Cole Gulino, Tristan Emrich, Zoey Yang, Shimon Whiteson, Brandyn White, Dragomir Anguelov p59151-59171 from Advances in Neural Information Processing Systems 36
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  • The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

    Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart Dalziel, Drummond Fielding, Daniel Fortunato, Jared Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich Kerswell, et al. p44989-45037 from Advances in Neural Information Processing Systems 37
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  • Theoretical Analysis of Deep Neural Networks for Temporally Dependent Observations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Theoretical Analysis of Deep Neural Networks for Temporally Dependent Observations

    Mingliang Ma, Abolfazl Safikhani p37324-37334 from Advances in Neural Information Processing Systems 35
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  • Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks

    Zihao Wang, Lei Wu p74289-74338 from Advances in Neural Information Processing Systems 36
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  • Theoretical Analysis of Weak-to-Strong Generalization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Theoretical Analysis of Weak-to-Strong Generalization

    Hunter Lang, David Sontag, Aravindan Vijayaraghavan p46837-46880 from Advances in Neural Information Processing Systems 37
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