31 citations · 71 across the 17 of their papers we have counts for
26 papers
Optimal Randomized Approximations for Matrix based Renyi's Entropy
Yuxin Dong, Tieliang Gong, Shujian Yu +1
The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions…
Multi-view Information Bottleneck Without Variational Approximation
Qi Zhang, Shujian Yu, Jingmin Xin +1
By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we ext…
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction
Yu Wang, Shuo Ye, Shujian Yu +1
Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences. E…
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
Hongming Li, Shujian Yu, Jose C. Principe
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{é…
Gated Information Bottleneck for Generalization in Sequential Environments
Francesco Alesiani, Shujian Yu, Xi Yu
Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum su…
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional
Xi Yu, Shujian Yu, Jose C. Principe
We introduce the matrix-based Renyi's -order entropy functional to parameterize Tishby et al. information bottleneck (IB) principle with a neural network. We term our methodolog…