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05/31/2023
Feature Learning in Image Hierarchies using Functional Maximal Correlation
This paper proposes the Hierarchical Functional Maximal Correlation Algo...
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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 Príncipe et al. in 2...
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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...
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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...
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11/03/2022
Robust Dependence Measure using RKHS based Uncertainty Moments and Optimal Transport
Reliable measurement of dependence between variables is essential in man...
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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...
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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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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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10/12/2021
Information Theoretic Structured Generative Modeling
Rényi's information provides a theoretical foundation for tractable and ...
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09/24/2021
Estimating Rényi's α-Cross-Entropies in a Matrix-Based Way
Conventional information-theoretic quantities assume access to probabili...
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09/22/2021
Quantifying Model Predictive Uncertainty with Perturbation Theory
We propose a framework for predictive uncertainty quantification of a ne...
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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...
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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...
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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 ...
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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...
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02/24/2021
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning
Reinforcement learning in large-scale environments is challenging due to...
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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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01/18/2021
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations
Deep-predictive-coding networks (DPCNs) are hierarchical, generative mod...
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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...
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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...
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10/18/2020
Unsupervised Foveal Vision Neural Networks with Top-Down Attention
Deep learning architectures are an extremely powerful tool for recognizi...
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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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05/05/2020
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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01/30/2020
Towards a Kernel based Physical Interpretation of Model Uncertainty
This paper introduces a new information theoretic framework that provide...
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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...
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12/10/2019
No-Trick (Treat) Kernel Adaptive Filtering using Deterministic Features
Kernel methods form a powerful, versatile, and theoretically-grounded un...
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11/24/2019
Functional Bayesian Filter
We present a general nonlinear Bayesian filter for high-dimensional stat...
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11/08/2019
Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image
To fully understand interactions between marine hydrokinetic (MHK) equip...
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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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04/14/2019
Minimum Error Entropy Kalman Filter
To date most linear and nonlinear Kalman filters (KFs) have been develop...
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04/13/2019
Maximum Correntropy Criterion with Variable Center
Correntropy is a local similarity measure defined in kernel space and th...
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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 ...
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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...
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12/31/2018
Theory and Algorithms for Pulse Signal Processing
The integrate and fire converter transforms an analog signal into train ...
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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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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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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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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...
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02/01/2018
Augmented Space Linear Model
The linear model uses the space defined by the input to project the targ...
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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 ...
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10/11/2017
Quantized Minimum Error Entropy Criterion
Comparing with traditional learning criteria, such as mean square error ...
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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 ...
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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 ...
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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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03/23/2017
Robustness of Maximum Correntropy Estimation Against Large Outliers
The maximum correntropy criterion (MCC) has recently been successfully a...
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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...
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10/31/2016
