2 citations · 5 across the 7 of their papers we have counts for
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stat.ML2023★ 1 cited
Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows
Mingxuan Yi, Song Liu
Variational inference is a technique that approximates a target distribution by optimizing within the parameter space of variational families. On the other hand, Wasserstein gradie…
stat.ML2023★ 1 cited
Minimizing -Divergences by Interpolating Velocity Fields
Song Liu, Jiahao Yu, Jack Simons +2
Many machine learning problems can be seen as approximating a \textit{target} distribution using a \textit{particle} distribution by minimizing their statistical discrepancy. Wasse…
stat.ML2023★ 2 cited
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
Mingxuan Yi, Zhanxing Zhu, Song Liu
The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator le…