174 citations · 191 across the 7 of their papers we have counts for
4 papers · 1 filter
Tensor Completion Leveraging Graph Information: A Dynamic Regularization Approach with Statistical Guarantees
Kaidong Wang, Qianxin Yi, Yao Wang +3
We consider the problem of tensor completion with graphs serving as side information to represent interrelationships among variables. Existing approaches suffer from several limita…
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…
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…
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…