b'Shujian Yu'

research

∙ 07/17/2023

Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study

Coping with distributional shifts is an important part of transfer learn...

0 Luis Pedro Silvestrin, et al. ∙

research

∙ 06/08/2023

Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) is a challenging task due to s...

0 Shuo Ye, et al. ∙

research

∙ 03/21/2023

Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy

Pairwise metrics are often employed to estimate statistical dependencies...

0 Qiang Li, et al. ∙

research

∙ 01/21/2023

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2...

0 Shujian Yu, et al. ∙

research

∙ 01/16/2023

Causal Recurrent Variational Autoencoder for Medical Time Series Generation

We propose causal recurrent variational autoencoder (CR-VAE), a novel ge...

0 Hongming Li, et al. ∙

research

∙ 01/04/2023

CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis

There is a recent trend to leverage the power of graph neural networks (...

0 Kaizhong Zheng, et al. ∙

research

∙ 11/30/2022

Robust and Fast Measure of Information via Low-rank Representation

The matrix-based Rényi's entropy allows us to directly quantify informat...

0 Yuxin Dong, et al. ∙

research

∙ 09/26/2022

Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval

Zero-shot cross-modal retrieval (ZS-CMR) deals with the retrieval proble...

0 Yufeng Shi, et al. ∙

research

∙ 05/31/2022

Principle of Relevant Information for Graph Sparsification

Graph sparsification aims to reduce the number of edges of a graph while...

0 Shujian Yu, et al. ∙

research

∙ 05/16/2022

Optimal Randomized Approximations for Matrix based Renyi's Entropy

The Matrix-based Renyi's entropy enables us to directly measure informat...

5 Yuxin Dong, et al. ∙

research

∙ 05/07/2022

BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

Developing a new diagnostic models based on the underlying biological me...

0 Kaizhong Zheng, et al. ∙

research

∙ 04/22/2022

Multi-view Information Bottleneck Without Variational Approximation

By "intelligently" fusing the complementary information across different...

0 Qi Zhang, et al. ∙

research

∙ 04/21/2022

R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction

Fine-grained visual categorization (FGVC) aims to discriminate similar s...

0 Yu Wang, et al. ∙

research

∙ 02/07/2022

Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing

We develop a new neural network based independent component analysis (IC...

0 Hongming Li, et al. ∙

research

∙ 12/27/2021

Computationally Efficient Approximations for Matrix-based Renyi's Entropy

The recently developed matrix based Renyi's entropy enables measurement ...

14 Tieliang Gong, et al. ∙

research

∙ 10/12/2021

Gated Information Bottleneck for Generalization in Sequential Environments

Deep neural networks suffer from poor generalization to unseen environme...

0 Francesco Alesiani, et al. ∙

research

∙ 10/12/2021

Information Theoretic Structured Generative Modeling

Rényi's information provides a theoretical foundation for tractable and ...

13 Bo Hu, et al. ∙

research

∙ 08/07/2021

Learning to Transfer with von Neumann Conditional Divergence

The similarity of feature representations plays a pivotal role in the su...

0 Ammar Shaker, et al. ∙

research

∙ 01/31/2021

Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional

We introduce the matrix-based Renyi's α-order entropy functional to para...

0 Xi Yu, et al. ∙

research

∙ 01/25/2021

Measuring Dependence with Matrix-based Entropy Functional

Measuring the dependence of data plays a central role in statistics and ...

0 Shujian Yu, et al. ∙

research

∙ 11/02/2020

Modular-Relatedness for Continual Learning

In this paper, we propose a continual learning (CL) technique that is be...

0 Ammar Shaker, et al. ∙

research

∙ 09/11/2020

Learning an Interpretable Graph Structure in Multi-Task Learning

We present a novel methodology to jointly perform multi-task learning an...

0 Shujian Yu, et al. ∙

research

∙ 09/11/2020

Towards Interpretable Multi-Task Learning Using Bilevel Programming

Interpretable Multi-Task Learning can be expressed as learning a sparse ...

0 Francesco Alesiani, et al. ∙

research

∙ 07/13/2020

PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders

Although substantial efforts have been made to learn disentangled repres...

0 Yanjun Li, et al. ∙

research

∙ 05/12/2020

Modularizing Deep Learning via Pairwise Learning With Kernels

By redefining the conventional notions of layers, we present an alternat...

0 Shiyu Duan, et al. ∙

research

∙ 05/05/2020

Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications

We propose a simple yet powerful test statistic to quantify the discrepa...

0 Shujian Yu, et al. ∙

research

∙ 09/25/2019

Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels

Analyzing deep neural networks (DNNs) via information plane (IP) theory ...

33 Kristoffer Wickstrøm, et al. ∙

research

∙ 07/13/2019

Multiscale Principle of Relevant Information for Hyperspectral Image Classification

This paper proposes a novel architecture, termed multiscale principle of...

0 Yantao Wei, et al. ∙

research

∙ 05/29/2019

Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation

Weakly-supervised semantic segmentation aims to assign each pixel a sema...

0 Zhengqiang Zhang, et al. ∙

research

∙ 11/29/2018

Simple stopping criteria for information theoretic feature selection

Information theoretic feature selection aims to select a smallest featur...

0 Shujian Yu, et al. ∙

research

∙ 11/19/2018

Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling

It remains a huge challenge to design effective and efficient trackers u...

0 Peng Zhang, et al. ∙

research

∙ 08/23/2018

Multivariate Extension of Matrix-based Renyi's α-order Entropy Functional

The matrix-based Renyi's α-order entropy functional was recently introdu...

0 Shujian Yu, et al. ∙

research

∙ 06/25/2018

Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels

One important assumption underlying common classification models is the ...

0 Shujian Yu, et al. ∙

research

∙ 05/21/2018

Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery

Despite the great potential of using the low-rank matrix recovery (LRMR)...

0 Qi Zheng, et al. ∙

research

∙ 04/19/2018

Multi-view Hybrid Embedding: A Divide-and-Conquer Approach

We present a novel cross-view classification algorithm where the gallery...

0 Jiamiao Xu, et al. ∙

research

∙ 04/18/2018

Understanding Convolutional Neural Network Training with Information Theory

Using information theoretic concepts to understand and explore the inner...

0 Shujian Yu, et al. ∙

research

∙ 03/30/2018

Understanding Autoencoders with Information Theoretic Concepts

Despite their great success in practical applications, there is still a ...

0 Shujian Yu, et al. ∙

research

∙ 07/25/2017

Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing

In a streaming environment, there is often a need for statistical predic...

0 Shujian Yu, et al. ∙

research

∙ 05/03/2017

Marine Animal Classification with Correntropy Loss Based Multi-view Learning

To analyze marine animals behavior, seasonal distribution and abundance,...

0 Zheng Cao, et al. ∙

Shujian Yu

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