Paper

  • Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

    Boyuan Chen, Yilun Du, Diego Martí Monsó, Max Simchowitz, Vincent Sitzmann, Russ Tedrake p24081-24125 from Advances in Neural Information Processing Systems 37
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  • Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence

    Trevor Darrell, Lisa Dunlap, Aleksander Holynski, Grace Luo, Dong Huk Park p47500-47510 from Advances in Neural Information Processing Systems 36
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  • Diffusion Imitation from Observation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Imitation from Observation

    Bo-Ruei Huang, Chun-Mao Lai, Shao-Hua Sun, Dai-Jie Wu, Chun-Kai Yang p137190-137217 from Advances in Neural Information Processing Systems 37
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  • Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks

    Hui Fang, Qiang He, Xin Yan p22326-22350 from Advances in Neural Information Processing Systems 36
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  • Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

    Chenjia Bai, Haoran He, Xuelong Li, Dong Wang, Kang Xu, Zhuoran Yang, Weinan Zhang, Bin Zhao p64896-64917 from Advances in Neural Information Processing Systems 36
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  • Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement

    Cuiling Lan, Yan Lu, Tao Yang, Nanning Zheng p82465-82492 from Advances in Neural Information Processing Systems 37
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  • Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

    Fan Bao, Chongxuan Li, Jiacheng Sun, Zebin You, Yong Zhong, Jun Zhu p43479-43495 from Advances in Neural Information Processing Systems 36
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  • Diffusion Models are Certifiably Robust Classifiers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Models are Certifiably Robust Classifiers

    Huanran Chen, Yinpeng Dong, Zhongkai Hao, Shitong Shao, Hang Su, Xiao Yang, Jun Zhu p50062-50097 from Advances in Neural Information Processing Systems 37
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  • Diffusion Models as Plug-And-Play Priors

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Models as Plug-And-Play Priors

    Alexandros Graikos, Nebojsa Jojic, Nikolay Malkin, Dimitris Samaras p14715-14728 from Advances in Neural Information Processing Systems 35
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  • Diffusion Models With Learned Adaptive Noise

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion Models With Learned Adaptive Noise

    Chris De, Aaron Gokaslan, Volodymyr Kuleshov, Subham Sahoo p105730-105779 from Advances in Neural Information Processing Systems 37
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  • Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models

    Wei Bi, Liheng Chen, Jiahui Gao, Shansan Gong, Xin Jiang, Lingpeng Kong, Zhenguo Li, Han Shi, Chuan Wu, Jiacheng Ye, Lin Zheng p105345-105374 from Advances in Neural Information Processing Systems 37
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  • Diffusion PID: Interpreting Diffusion via Partial Information Decomposition

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Diffusion PID: Interpreting Diffusion via Partial Information Decomposition

    Yonatan Bisk, Yingshan Chang, Shaurya Dewan, Andrew Luo, Prakanshul Saxena, Rushikesh Zawar p2045-2079 from Advances in Neural Information Processing Systems 37
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