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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...
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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...
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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...
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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 PrÃncipe et al. in 2...
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01/16/2023
Causal Recurrent Variational Autoencoder for Medical Time Series Generation
We propose causal recurrent variational autoencoder (CR-VAE), a novel ge...
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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 (...
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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...
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09/26/2022
Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval
Zero-shot cross-modal retrieval (ZS-CMR) deals with the retrieval proble...
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05/31/2022
Principle of Relevant Information for Graph Sparsification
Graph sparsification aims to reduce the number of edges of a graph while...
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05/16/2022
Optimal Randomized Approximations for Matrix based Renyi's Entropy
The Matrix-based Renyi's entropy enables us to directly measure informat...
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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...
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04/22/2022
Multi-view Information Bottleneck Without Variational Approximation
By "intelligently" fusing the complementary information across different...
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04/21/2022
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction
Fine-grained visual categorization (FGVC) aims to discriminate similar s...
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02/07/2022
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
We develop a new neural network based independent component analysis (IC...
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12/27/2021
Computationally Efficient Approximations for Matrix-based Renyi's Entropy
The recently developed matrix based Renyi's entropy enables measurement ...
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10/12/2021
Gated Information Bottleneck for Generalization in Sequential Environments
Deep neural networks suffer from poor generalization to unseen environme...
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10/12/2021
Information Theoretic Structured Generative Modeling
Rényi's information provides a theoretical foundation for tractable and ...
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08/07/2021
Learning to Transfer with von Neumann Conditional Divergence
The similarity of feature representations plays a pivotal role in the su...
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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...
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01/25/2021
Measuring Dependence with Matrix-based Entropy Functional
Measuring the dependence of data plays a central role in statistics and ...
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11/02/2020
Modular-Relatedness for Continual Learning
In this paper, we propose a continual learning (CL) technique that is be...
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09/11/2020
Learning an Interpretable Graph Structure in Multi-Task Learning
We present a novel methodology to jointly perform multi-task learning an...
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09/11/2020
Towards Interpretable Multi-Task Learning Using Bilevel Programming
Interpretable Multi-Task Learning can be expressed as learning a sparse ...
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07/13/2020
PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders
Although substantial efforts have been made to learn disentangled repres...
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05/12/2020
Modularizing Deep Learning via Pairwise Learning With Kernels
By redefining the conventional notions of layers, we present an alternat...
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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...
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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 ...
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07/13/2019
Multiscale Principle of Relevant Information for Hyperspectral Image Classification
This paper proposes a novel architecture, termed multiscale principle of...
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05/29/2019
Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation aims to assign each pixel a sema...
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11/29/2018
Simple stopping criteria for information theoretic feature selection
Information theoretic feature selection aims to select a smallest featur...
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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...
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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...
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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 ...
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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)...
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04/19/2018
Multi-view Hybrid Embedding: A Divide-and-Conquer Approach
We present a novel cross-view classification algorithm where the gallery...
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04/18/2018
Understanding Convolutional Neural Network Training with Information Theory
Using information theoretic concepts to understand and explore the inner...
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03/30/2018
Understanding Autoencoders with Information Theoretic Concepts
Despite their great success in practical applications, there is still a ...
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07/25/2017
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing
In a streaming environment, there is often a need for statistical predic...
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05/03/2017
