Shujian Yu

Understanding Convolutional Neural Networks with Information Theory: An Initial Exploration

2018 · arXiv (Cornell University)

The matrix-based Renyi's α-entropy functional and its multivariate extension were recently developed in terms of the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel Hilbert space (RKHS). However, the …

Gated Information Bottleneck for Generalization in Sequential Environments

2021 · arXiv (Cornell University)

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 sufficient representations from training data, the information bottleneck (IB) …

Towards a More Stable and General Subgraph Information Bottleneck

2023

Graph Neural Networks (GNNs) have been widely applied to graph-structured data. However, the lack of interpretability impedes its practical deployment especially in high-risk areas such as medical diagnosis. Recently, the Information Bottleneck (IB) principle has …

Deep Dynamic Probabilistic Canonical Correlation Analysis

2025

This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D2PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Building on the probabilistic extensions of Canonical Correlation Analysis (CCA), D2PCCA …