1 citations · 2 across the 6 of their papers we have counts for
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Wasserstein Convergence of ODE-Based Samplers in Decentralized Diffusion Model via Velocity Field Decomposition
Chencheng Tang, Xuanyu Xue, Fangyikang Wang +2
Diffusion models have achieved impressive empirical success in generative tasks, and their convergence theory is now relatively well understood. Motivated by privacy and scalabilit…
Efficiently Access Diffusion Fisher: Within the Outer Product Span Space
Fangyikang Wang, Hubery Yin, Shaobin Zhuang +7
Recent Diffusion models (DMs) advancements have explored incorporating the second-order diffusion Fisher information (DF), defined as the negative Hessian of log density, into vari…
Neural Sinkhorn Gradient Flow
Huminhao Zhu, Fangyikang Wang, Chao Zhang +2
Wasserstein Gradient Flows (WGF) with respect to specific functionals have been widely used in the machine learning literature. Recently, neural networks have been adopted to appro…
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework
Fangyikang Wang, Huminhao Zhu, Chao Zhang +2
Particle-based Variational Inference (ParVI) methods approximate the target distribution by iteratively evolving finite weighted particle systems. Recent advances of ParVI methods…