Paper

  • StyleGAN knows Normal, Depth, Albedo, and More

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    StyleGAN knows Normal, Depth, Albedo, and More

    Anand Bhattad, David Forsyth, Derek Hoiem, Daniel McKee p73082-73103 from Advances in Neural Information Processing Systems 36
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  • StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

    Cong Han, Yinghao Aaron Li, Nima Mesgarani, Gavin Mischler, Vinay Raghavan p19594-19621 from Advances in Neural Information Processing Systems 36
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  • Stylus: Automatic Adapter Selection for Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Stylus: Automatic Adapter Selection for Diffusion Models

    Zhifeng Chen, Joseph Gonzalez, Yanping Huang, Michael Luo, Ruslan Salakhutdinov, Ion Stoica, Brandon Trabucco, Justin Wong p32888-32915 from Advances in Neural Information Processing Systems 37
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  • Sub-Exponential Time Sum-Of-Squares Lower Bounds for Principal Components Analysis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Sub-Exponential Time Sum-Of-Squares Lower Bounds for Principal Components Analysis

    Aaron Potechin, Goutham Rajendran p35724-35740 from Advances in Neural Information Processing Systems 35
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  • Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning

    Gabriele Curti, Alberto Metelli, Riccardo Poiani, Marcello Restelli p85778-85823 from Advances in Neural Information Processing Systems 37
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  • Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression

    Jiaze Qiu p63874-63893 from Advances in Neural Information Processing Systems 36
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  • Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping

    Yingbin Bai, Bo Han, Zhongyi Han, Tongliang Liu, Dadong Wang, Erkun Yang, Jun Yu p68343-68366 from Advances in Neural Information Processing Systems 36
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  • Subgame Solving in Adversarial Team Games

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Subgame Solving in Adversarial Team Games

    Federico Cacciamani, Luca Carminati, Gabriele Farina, Nicola Gatti, Pierriccardo Olivieri, Tuomas Sandholm, Brian Zhang p26686-26697 from Advances in Neural Information Processing Systems 35
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  • SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning

    Bin Feng, Yu Li, Zijing Liu, Yu Wang, Jiying Zhang p29620-29656 from Advances in Neural Information Processing Systems 37
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  • Subgroup Robustness Grows on Trees: An Empirical Baseline Investigation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Subgroup Robustness Grows on Trees: An Empirical Baseline Investigation

    Josh Gardner, Zoran Popovic, Ludwig Schmidt p9939-9954 from Advances in Neural Information Processing Systems 35
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  • Subject-driven Text-to-Image Generation via Apprenticeship Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Subject-driven Text-to-Image Generation via Apprenticeship Learning

    Ming-Wei Chang, Wenhu Chen, William Cohen, Hexiang Hu, Xuhui Jia, Yandong Li, Nataniel Ruiz p30286-30305 from Advances in Neural Information Processing Systems 36
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  • Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning

    Suraj Kothawade, Yeqing Li, William Loh, Yanting Miao, Pascal Poupart, Abdullah Rashwan p123563-123591 from Advances in Neural Information Processing Systems 37
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