49 citations · 114 across the 17 of their papers we have counts for
7 papers · 1 filter
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Chenwei Ding, Mingming Gong, Kun Zhang +1
Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, non-Gaussian noise to achieve id…
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Yipeng Mou, Mingming Gong, Huan Fu +3
Majority of state-of-the-art monocular depth estimation methods are supervised learning approaches. The success of such approaches heavily depends on the high-quality depth labels…
Twin Auxiliary Classifiers GAN
Mingming Gong, Yanwu Xu, Chunyuan Li +2
Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly…
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
Biwei Huang, Kun Zhang, Mingming Gong +1
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting t…
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Shanshan Zhao, Huan Fu, Mingming Gong +1
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures. Since the groundtruth depth labels are hard to obtain, recent methods try to…
Generative-Discriminative Complementary Learning
Yanwu Xu, Mingming Gong, Junxiang Chen +3
Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such appr…