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• 2022 : P24: Unifying Generative Models with GFlowNets »
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• 2023 : Neural Optimal Transport with Lagrangian Costs »
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• 2023 : On Convergence of Approximate Schr\"{o}dinger Bridge with Bounded Cost »
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• 2023 : TaskMet: Task-Driven Metric Learning for Model Learning »
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• 2023 Workshop: Structured Probabilistic Inference and Generative Modeling »
  Dinghuai Zhang · Yuanqi Du · Chenlin Meng · Shawn Tan · Yingzhen Li · Max Welling · Yoshua Bengio
• 2023 : Opening Remark »
  Dinghuai Zhang · Yuanqi Du · Chenlin Meng · Shawn Tan · Yingzhen Li · Max Welling · Yoshua Bengio
• 2023 Poster: PFGM++: Unlocking the Potential of Physics-Inspired Generative Models »
  Yilun Xu · Ziming Liu · Yonglong Tian · Shangyuan Tong · Max Tegmark · Tommi Jaakkola
• 2023 Poster: Fast Sampling of Diffusion Models via Operator Learning »
  Hongkai Zheng · Weili Nie · Arash Vahdat · Kamyar Azizzadenesheli · Anima Anandkumar
• 2023 Poster: Consistency Models »
  Yang Song · Prafulla Dhariwal · Mark Chen · Ilya Sutskever
• 2023 Poster: I$^2$SB: Image-to-Image Schrödinger Bridge »
  Guan-Horng Liu · Arash Vahdat · De-An Huang · Evangelos Theodorou · Weili Nie · Anima Anandkumar
• 2023 Poster: Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation »
  Jiaming Song · Qinsheng Zhang · Hongxu Yin · Morteza Mardani · Ming-Yu Liu · Jan Kautz · Yongxin Chen · Arash Vahdat
• 2023 Poster: Multisample Flow Matching: Straightening Flows with Minibatch Couplings »
  Aram-Alexandre Pooladian · Heli Ben-Hamu · Carles Domingo i Enrich · Brandon Amos · Yaron Lipman · Ricky T. Q. Chen
• 2023 Poster: On Kinetic Optimal Probability Paths for Generative Models »
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• 2022 : Neural Geometric Embedding Flows »
  Aaron Lou · Yang Song · Jiaming Song · Stefano Ermon
• 2022 Poster: Diffusion Models for Adversarial Purification »
  Weili Nie · Brandon Guo · Yujia Huang · Chaowei Xiao · Arash Vahdat · Animashree Anandkumar
• 2022 Spotlight: Diffusion Models for Adversarial Purification »
  Weili Nie · Brandon Guo · Yujia Huang · Chaowei Xiao · Arash Vahdat · Animashree Anandkumar
• 2022 Poster: ButterflyFlow: Building Invertible Layers with Butterfly Matrices »
  Chenlin Meng · Linqi Zhou · Kristy Choi · Tri Dao · Stefano Ermon
• 2022 Poster: Matching Normalizing Flows and Probability Paths on Manifolds »
  Heli Ben-Hamu · samuel cohen · Joey Bose · Brandon Amos · Maximilian Nickel · Aditya Grover · Ricky T. Q. Chen · Yaron Lipman
• 2022 Spotlight: Matching Normalizing Flows and Probability Paths on Manifolds »
  Heli Ben-Hamu · samuel cohen · Joey Bose · Brandon Amos · Maximilian Nickel · Aditya Grover · Ricky T. Q. Chen · Yaron Lipman
• 2022 Spotlight: ButterflyFlow: Building Invertible Layers with Butterfly Matrices »
  Chenlin Meng · Linqi Zhou · Kristy Choi · Tri Dao · Stefano Ermon
• 2021 Poster: Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving »
  Yang Song · Chenlin Meng · Renjie Liao · Stefano Ermon
• 2021 Poster: Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? »
  Dinghuai Zhang · Kartik Ahuja · Yilun Xu · Yisen Wang · Aaron Courville
• 2021 Spotlight: Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving »
  Yang Song · Chenlin Meng · Renjie Liao · Stefano Ermon
• 2021 Oral: Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? »
  Dinghuai Zhang · Kartik Ahuja · Yilun Xu · Yisen Wang · Aaron Courville
• 2020 Poster: Undirected Graphical Models as Approximate Posteriors »
  Arash Vahdat · Evgeny Andriyash · William Macready
• 2020 Poster: Training Deep Energy-Based Models with f-Divergence Minimization »
  Lantao Yu · Yang Song · Jiaming Song · Stefano Ermon
• 2018 Poster: Accelerating Natural Gradient with Higher-Order Invariance »
  Yang Song · Jiaming Song · Stefano Ermon
• 2018 Oral: Accelerating Natural Gradient with Higher-Order Invariance »
  Yang Song · Jiaming Song · Stefano Ermon