Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities

    Aleksandr Beznosikov, Alexander Gasnikov, Martin Takac p28663-28677 from Advances in Neural Information Processing Systems 36
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  • Similarity-based cooperative equilibrium

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Similarity-based cooperative equilibrium

    Vincent Conitzer, Jakob Foerster, Roger Grosse, Caspar Oesterheld, Johannes Treutlein p24434-24465 from Advances in Neural Information Processing Systems 36
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  • Similarity-Navigated Conformal Prediction for Graph Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Similarity-Navigated Conformal Prediction for Graph Neural Networks

    Jianguo Huang, Wenyu Jiang, Shuangjie Li, Jianqing Song, Chongjun Wang, Baoming Zhang p48541-48567 from Advances in Neural Information Processing Systems 37
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  • SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

    Eleni Chatzi, Hao Dong, Olga Fink, Ismail Nejjar, Han Sun p78674-78695 from Advances in Neural Information Processing Systems 36
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  • SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

    Jiaxiang Dong, Mingsheng Long, Jianmin Wang, Haixu Wu, Haoran Zhang, Li Zhang p29996-30025 from Advances in Neural Information Processing Systems 36
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  • Simple and Asymmetric Graph Contrastive Learning without Augmentations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple and Asymmetric Graph Contrastive Learning without Augmentations

    Zhengyu Chen, Suhang Wang, Teng Xiao, Huaisheng Zhu p16129-16152 from Advances in Neural Information Processing Systems 36
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  • Simple and Controllable Music Generation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple and Controllable Music Generation

    Yossi Adi, Jade Copet, Alexandre Defossez, Itai Gat, David Kant, Felix Kreuk, Tal Remez, Gabriel Synnaeve p47704-47720 from Advances in Neural Information Processing Systems 36
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  • Simple and Effective Masked Diffusion Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple and Effective Masked Diffusion Language Models

    Marianne Arriola, Justin Chiu, Aaron Gokaslan, Volodymyr Kuleshov, Edgar Marroquin, Alexander Rush, Subham Sahoo, Yair Schiff p130136-130184 from Advances in Neural Information Processing Systems 37
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  • Simple and Fast Distillation of Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple and Fast Distillation of Diffusion Models

    Chun Chen, Defang Chen, Siwei Lyu, Can Wang, Zhenyu Zhou p40831-40860 from Advances in Neural Information Processing Systems 37
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  • Simple and Optimal Greedy Online Contention Resolution Schemes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple and Optimal Greedy Online Contention Resolution Schemes

    Vasilis Livanos p9687-9699 from Advances in Neural Information Processing Systems 35
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  • Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions

    Gagan Aggarwal, Kshipra Bhawalkar, Aranyak Mehta, Divyarthi Mohan, Alexandros Psomas p28280-28292 from Advances in Neural Information Processing Systems 35
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  • Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos

    Sungjin Ahn, Gautam Singh, Yi-Fu Wu p18181-18196 from Advances in Neural Information Processing Systems 35
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  • Simple, Scalable and Effective Clustering via One-Dimensional Projections

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simple, Scalable and Effective Clustering via One-Dimensional Projections

    Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten p64618-64649 from Advances in Neural Information Processing Systems 36
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  • Simplicity Bias in 1-Hidden Layer Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simplicity Bias in 1-Hidden Layer Neural Networks

    Jatin Batra, Prateek Jain, Depen Morwani, Praneeth Netrapalli p8048-8075 from Advances in Neural Information Processing Systems 36
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  • Simplified and Generalized Masked Diffusion for Discrete Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simplified and Generalized Masked Diffusion for Discrete Data

    Arnaud Doucet, Kehang Han, Jiaxin Shi, Michalis Titsias, Zhe Wang p103131-103167 from Advances in Neural Information Processing Systems 37
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  • Simplified Graph Convolution with Heterophily

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simplified Graph Convolution with Heterophily

    Sudhanshu Chanpuriya, Cameron Musco p27184-27197 from Advances in Neural Information Processing Systems 35
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  • Simplifying Constraint Inference with Inverse Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simplifying Constraint Inference with Inverse Reinforcement Learning

    Glen Berseth, Adriana Hugessen, Harley Wiltzer p44501-44525 from Advances in Neural Information Processing Systems 37
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  • Simplifying Latent Dynamics with Softly State-Invariant World Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simplifying Latent Dynamics with Softly State-Invariant World Models

    Peter Dayan, Tankred Saanum, Eric Schulz p38355-38382 from Advances in Neural Information Processing Systems 37
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  • Simplifying Neural Network Training Under Class Imbalance

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simplifying Neural Network Training Under Class Imbalance

    C. Bayan Bruss, Micah Goldblum, Yucen Li, Ravid Shwartz-Ziv, Andrew Wilson p35218-35245 from Advances in Neural Information Processing Systems 36
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  • SimPO: Simple Preference Optimization with a Reference-Free Reward

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    SimPO: Simple Preference Optimization with a Reference-Free Reward

    Danqi Chen, Yu Meng, Mengzhou Xia p124198-124235 from Advances in Neural Information Processing Systems 37
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  • Simulation-Free Training of Neural ODEs on Paired Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simulation-Free Training of Neural ODEs on Paired Data

    Yeonwoo Cha, Seunghoon Hong, Jinwoo Kim, Saehoon Kim, Semin Kim, Jaehoon Yoo p60212-60236 from Advances in Neural Information Processing Systems 37
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  • Simulation-Guided Beam Search for Neural Combinatorial Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simulation-Guided Beam Search for Neural Combinatorial Optimization

    Jinho Choo, Youngjune Gwon, André Hottung, Jeongwoo Jae, Jihoon Kim, Yeong-Dae Kwon, Kevin Tierney p8760-8772 from Advances in Neural Information Processing Systems 35
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  • Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization

    Haggai Agmon, Yoram Burak p16770-16782 from Advances in Neural Information Processing Systems 36
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  • Simultaneous Missing Value Imputation and Structure Learning with Groups

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Simultaneous Missing Value Imputation and Structure Learning with Groups

    Miltiadis Allamanis, Wenbo Gong, Angus Lamb, Pablo Morales-Alvarez, Nick Pawlowski, Simon Peyton Jones, Simon Woodhead, Cheng Zhang p20011-20024 from Advances in Neural Information Processing Systems 35
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