Jose C. Principe

research

∙ 05/31/2023

Feature Learning in Image Hierarchies using Functional Maximal Correlation

This paper proposes the Hierarchical Functional Maximal Correlation Algo...

0 Bo Hu, 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

∙ 12/20/2022

Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning

In this paper, we consider the problem of adjusting the exploration rate...

0 Isaac J. Sledge, et al. ∙

research

∙ 12/09/2022

The Cross Density Kernel Function: A Novel Framework to Quantify Statistical Dependence for Random Processes

This paper proposes a novel multivariate definition of statistical depen...

0 Bo Hu, et al. ∙

research

∙ 11/03/2022

Robust Dependence Measure using RKHS based Uncertainty Moments and Optimal Transport

Reliable measurement of dependence between variables is essential in man...

0 Rishabh Singh, et al. ∙

research

∙ 11/03/2022

Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS

Deep learning models for semantic segmentation are prone to poor perform...

0 Rishabh Singh, 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

∙ 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

∙ 10/12/2021

Information Theoretic Structured Generative Modeling

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

13 Bo Hu, et al. ∙

research

∙ 09/24/2021

Estimating Rényi's α-Cross-Entropies in a Matrix-Based Way

Conventional information-theoretic quantities assume access to probabili...

0 Isaac J. Sledge, et al. ∙

research

∙ 09/22/2021

Quantifying Model Predictive Uncertainty with Perturbation Theory

We propose a framework for predictive uncertainty quantification of a ne...

5 Rishabh Singh, et al. ∙

research

∙ 07/22/2021

External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery

We propose a memory-based framework for real-time, data-efficient target...

8 Isaac J. Sledge, et al. ∙

research

∙ 07/05/2021

An Information-Theoretic Approach for Automatically Determining the Number of States when Aggregating Markov Chains

A fundamental problem when aggregating Markov chains is the specificatio...

0 Isaac J. Sledge, et al. ∙

research

∙ 04/19/2021

Labels, Information, and Computation: Efficient, Privacy-Preserving Learning Using Sufficient Labels

In supervised learning, obtaining a large set of fully-labeled training ...

0 Shiyu Duan, et al. ∙

research

∙ 03/02/2021

A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts

Traditional Bayesian approaches for model uncertainty quantification rel...

16 Rishabh Singh, et al. ∙

research

∙ 02/24/2021

Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning

Reinforcement learning in large-scale environments is challenging due to...

6 Isaac J. Sledge, 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

∙ 01/18/2021

Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations

Deep-predictive-coding networks (DPCNs) are hierarchical, generative mod...

21 Isaac J. Sledge, et al. ∙

research

∙ 01/10/2021

Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders

We propose a saliency-based, multi-target detection and segmentation fra...

9 Isaac J. Sledge, et al. ∙

research

∙ 01/09/2021

Training Deep Architectures Without End-to-End Backpropagation: A Brief Survey

This tutorial paper surveys training alternatives to end-to-end backprop...

0 Shiyu Duan, et al. ∙

research

∙ 10/18/2020

Unsupervised Foveal Vision Neural Networks with Top-Down Attention

Deep learning architectures are an extremely powerful tool for recognizi...

14 Ryan Burt, 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

∙ 05/05/2020

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

∙ 01/30/2020

Towards a Kernel based Physical Interpretation of Model Uncertainty

This paper introduces a new information theoretic framework that provide...

22 Rishabh Singh, et al. ∙

research

∙ 01/01/2020

Fast Estimation of Information Theoretic Learning Descriptors using Explicit Inner Product Spaces

Kernel methods form a theoretically-grounded, powerful and versatile fra...

19 Kan Li, et al. ∙

research

∙ 12/10/2019

No-Trick (Treat) Kernel Adaptive Filtering using Deterministic Features

Kernel methods form a powerful, versatile, and theoretically-grounded un...

44 Kan Li, et al. ∙

research

∙ 11/24/2019

Functional Bayesian Filter

We present a general nonlinear Bayesian filter for high-dimensional stat...

0 Kan Li, et al. ∙

research

∙ 11/08/2019

Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image

To fully understand interactions between marine hydrokinetic (MHK) equip...

17 Chi Ding, 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

∙ 04/14/2019

Minimum Error Entropy Kalman Filter

To date most linear and nonlinear Kalman filters (KFs) have been develop...

0 Badong Chen, et al. ∙

research

∙ 04/13/2019

Maximum Correntropy Criterion with Variable Center

Correntropy is a local similarity measure defined in kernel space and th...

0 Badong Chen, et al. ∙

research

∙ 03/21/2019

Reduction of Markov Chains using a Value-of-Information-Based Approach

In this paper, we propose an approach to obtain reduced-order models of ...

0 Isaac J. Sledge, et al. ∙

research

∙ 01/22/2019

An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations

Radial-basis-function networks are traditionally defined for sets of vec...

14 Isaac J. Sledge, et al. ∙

research

∙ 12/31/2018

Theory and Algorithms for Pulse Signal Processing

The integrate and fire converter transforms an analog signal into train ...

0 Gabriel Nallathambi, 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

∙ 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

∙ 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

∙ 02/05/2018

Guided Policy Exploration for Markov Decision Processes using an Uncertainty-Based Value-of-Information Criterion

Reinforcement learning in environments with many action-state pairs is c...

0 Isaac J. Sledge, et al. ∙

research

∙ 02/01/2018

Augmented Space Linear Model

The linear model uses the space defined by the input to project the targ...

0 Zhengda Qin, et al. ∙

research

∙ 10/28/2017

Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information

In this paper, we provide an approach to clustering relational matrices ...

0 Isaac J. Sledge, et al. ∙

research

∙ 10/11/2017

Quantized Minimum Error Entropy Criterion

Comparing with traditional learning criteria, such as mean square error ...

0 Badong Chen, et al. ∙

research

∙ 10/08/2017

Using the Value of Information to Explore Stochastic, Discrete Multi-Armed Bandits

In this paper, we propose an information-theoretic exploration strategy ...

0 Isaac J. Sledge, et al. ∙

research

∙ 08/04/2017

Associations among Image Assessments as Cost Functions in Linear Decomposition: MSE, SSIM, and Correlation Coefficient

The traditional methods of image assessment, such as mean squared error ...

0 Jianji Wang, 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

∙ 03/23/2017

Robustness of Maximum Correntropy Estimation Against Large Outliers

The maximum correntropy criterion (MCC) has recently been successfully a...

0 Badong Chen, et al. ∙

research

∙ 02/28/2017

Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies

Conventional reinforcement learning methods for Markov decision processe...

0 Isaac J. Sledge, et al. ∙

research

∙ 10/31/2016

Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks

We propose a convolutional recurrent neural network, with Winner-Take-Al...

0 Eder Santana, et al. ∙

Jose C. Principe

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