5 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 4 cited
Deep Generative Learning via Schrödinger Bridge
Gefei Wang, Yuling Jiao, Qian Xu +2
We propose to learn a generative model via entropy interpolation with a Schrödinger Bridge. The generative learning task can be formulated as interpolating between a reference dist…
cs.LG2019★ 5 cited
Wasserstein-Wasserstein Auto-Encoders
Shunkang Zhang, Yuan Gao, Yuling Jiao +3
To address the challenges in learning deep generative models (e.g.,the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we pr…
cs.LG2019★ 4 cited
Deep Generative Learning via Variational Gradient Flow
Yuan Gao, Yuling Jiao, Yang Wang +3
We propose a general framework to learn deep generative models via \textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability spaces. The evolving distribution tha…