9 citations · 12 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 9 cited
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality
Fei Ma, Xiangxiang Xu, Shao-Lun Huang +1
Multimodal learning has achieved great successes in many scenarios. Compared with unimodal learning, it can effectively combine the information from different modalities to improve…
cs.IT2019★ 3 cited
An Information Theoretic Interpretation to Deep Neural Networks
Shao-Lun Huang, Xiangxiang Xu, Lizhong Zheng +1
It is commonly believed that the hidden layers of deep neural networks (DNNs) attempt to extract informative features for learning tasks. In this paper, we formalize this intuition…
cs.LG2018
An Efficient Approach to Informative Feature Extraction from Multimodal Data
Lichen Wang, Jiaxiang Wu, Shao-Lun Huang +4
One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the…