Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Contrastive Learning as Goal-Conditioned Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Learning as Goal-Conditioned Reinforcement Learning

    Benjamin Eysenbach, Sergey Levine, Russ Salakhutdinov, Tianjun Zhang p35603-35620 from Advances in Neural Information Processing Systems 35
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  • Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion

    Yash Bhalgat, João Henriques, Iro Laina, Andrea Vedaldi, Andrew Zisserman p9092-9116 from Advances in Neural Information Processing Systems 36
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  • Contrastive losses as generalized models of global epistasis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive losses as generalized models of global epistasis

    David Brookes, Jakub Otwinowski, Sam Sinai p93374-93405 from Advances in Neural Information Processing Systems 37
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  • Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning

    Ruizhi Chen, Yunji Chen, Zidong Du, Yunkai Gao, Jiaming Guo, Xing Hu, Siming Lan, Ling Li, Shaohui Peng, Fan Wu, Qi Yi, Xishan Zhang, Rui Zhang p36507-36523 from Advances in Neural Information Processing Systems 36
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  • Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time

    Xinyuan Cao, Santosh Vempala p72540-72582 from Advances in Neural Information Processing Systems 36
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  • Contrastive Neural Ratio Estimation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Neural Ratio Estimation

    Patrick Forré, Benjamin K Miller, Christoph Weniger p3262-3278 from Advances in Neural Information Processing Systems 35
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  • Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL

    Benjamin Alsbury-Nealy, Yoshua Bengio, Thomas Jiralerspong, Dane Malenfant, Blake Richards, Chen Sun, Wannan Yang p31117-31139 from Advances in Neural Information Processing Systems 36
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  • Contrastive Sampling Chains in Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Sampling Chains in Diffusion Models

    Daochang Liu, Chang Xu, Junyu Zhang, Shichao Zhang p73524-73542 from Advances in Neural Information Processing Systems 36
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  • Contrastive Training of Complex-Valued Autoencoders for Object Discovery

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive Training of Complex-Valued Autoencoders for Object Discovery

    Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Aleksandar Stanic p11075-11101 from Advances in Neural Information Processing Systems 36
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  • Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT

    SueYeon Chung, Jenelle Feather, Eero Simoncelli, Thomas Yerxa p96045-96070 from Advances in Neural Information Processing Systems 37
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  • Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

    Zhaoyang Huang, Hongsheng Li, Keqiang Sun, Quan Wang, Shangzhe Wu, Ning Zhang p16331-16343 from Advances in Neural Information Processing Systems 35
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  • Controllable Text Generation with Neurally-Decomposed Oracle

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controllable Text Generation with Neurally-Decomposed Oracle

    Kai-Wei Chang, Sidi Lu, Tao Meng, Nanyun Peng p28125-28139 from Advances in Neural Information Processing Systems 35
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  • Controlled maximal variability along with reliable performance in recurrent neural networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controlled maximal variability along with reliable performance in recurrent neural networks

    Chiara Mastrogiuseppe, Rubén Moreno-Bote p24569-24600 from Advances in Neural Information Processing Systems 37
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  • Controlled Sparsity Via Constrained Optimization Or: How I Learned to Stop Tuning Penalties and Love Constraints

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controlled Sparsity Via Constrained Optimization Or: How I Learned to Stop Tuning Penalties and Love Constraints

    Yoshua Bengio, Akram Erraqabi, Jose Gallego-Posada, Simon Lacoste-Julien, Juan Ramirez p1253-1266 from Advances in Neural Information Processing Systems 35
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  • Controlling Continuous Relaxation for Combinatorial Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controlling Continuous Relaxation for Combinatorial Optimization

    Yuma Ichikawa p47189-47216 from Advances in Neural Information Processing Systems 37
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  • Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets

    Manuel Rodriguez, Eleni Straitouri, Suhas Thejaswi p129443-129479 from Advances in Neural Information Processing Systems 37
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  • Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound

    Reuben Adams, Benjamin Guedj, John Shawe-Taylor p5308-5337 from Advances in Neural Information Processing Systems 37
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  • Controlling Text-to-Image Diffusion by Orthogonal Finetuning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Controlling Text-to-Image Diffusion by Orthogonal Finetuning

    Haiwen Feng, Yao Feng, Weiyang Liu, Zhen Liu, Zeju Qiu, Bernhard Schölkopf, Adrian Weller, Yuxuan Xue, Dan Zhang p79320-79362 from Advances in Neural Information Processing Systems 36
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  • ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models

    Xinyue Cai, Hao Fei, Oucheng Huang, Jiayi Ji, Rongrong Ji, Guannan Jiang, Jiale Li, Luo, Xiaoshuai Sun, Mingrui Wu p45206-45234 from Advances in Neural Information Processing Systems 37
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  • ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence

    Shihua Li, Wenjie Mei, Dongzhe Zheng p99232-99261 from Advances in Neural Information Processing Systems 37
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  • ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Ablation Capability for Large Vision-Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Ablation Capability for Large Vision-Language Models

    Chuanhao Li, Yuqi Lin, Shuo Liu, Ping Luo, Yu Qiao, Wenqi Shao, Yue Yang, Kaining Ying, Hao Zhang, Kaipeng Zhang, Tianle Zhang p100734-100782 from Advances in Neural Information Processing Systems 37
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  • Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels

    Jungbin Kim, Insoon Yang p63023-63035 from Advances in Neural Information Processing Systems 36
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  • Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

    Yipeng Li, Xinchen Lyu p56700-56755 from Advances in Neural Information Processing Systems 36
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  • Convergence Analysis of Split Federated Learning on Heterogeneous Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Convergence Analysis of Split Federated Learning on Heterogeneous Data

    Pengchao Han, Chao Huang, Xin Liu, Ming Tang, Geng Tian p103476-103544 from Advances in Neural Information Processing Systems 37
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