224 citations · 273 across the 11 of their papers we have counts for
8 papers · 1 filter
Triple Generative Adversarial Networks
Chongxuan Li, Kun Xu, Jiashuo Liu +2
We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax gam…
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure
Zhijie Deng, Yucen Luo, Jun Zhu +1
Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights. However, such models face the challenge of Bayes…
Understanding and Stabilizing GANs' Training Dynamics with Control Theory
Kun Xu, Chongxuan Li, Jun Zhu +1
Generative adversarial networks (GANs) are effective in generating realistic images but the training is often unstable. There are existing efforts that model the training dynamics…
Multi-objects Generation with Amortized Structural Regularization
Kun Xu, Chongxuan Li, Jun Zhu +1
Deep generative models (DGMs) have shown promise in image generation. However, most of the existing work learn the model by simply optimizing a divergence between the marginal dist…
To Relieve Your Headache of Training an MRF, Take AdVIL
Chongxuan Li, Chao Du, Kun Xu +3
We propose a black-box algorithm called {\it Adversarial Variational Inference and Learning} (AdVIL) to perform inference and learning on a general Markov random field (MRF). AdVIL…
Triple Generative Adversarial Nets
Chongxuan Li, Kun Xu, Jun Zhu +1
Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and…
Collaborative Filtering with User-Item Co-Autoregressive Models
Chao Du, Chongxuan Li, Yin Zheng +2
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the unde…
Big Learning with Bayesian Methods
Jun Zhu, Jianfei Chen, Wenbo Hu +1
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning…