
Unsupervised Foveal Vision Neural Networks with TopDown Attention
Deep learning architectures are an extremely powerful tool for recognizi...
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PRIVAE: PrincipleofRelevantInformation Variational Autoencoders
Although substantial efforts have been made to learn disentangled repres...
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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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Towards a Kernel based Physical Interpretation of Model Uncertainty
This paper introduces a new information theoretic framework that provide...
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Fast Estimation of Information Theoretic Learning Descriptors using Explicit Inner Product Spaces
Kernel methods form a theoreticallygrounded, powerful and versatile fra...
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NoTrick (Treat) Kernel Adaptive Filtering using Deterministic Features
Kernel methods form a powerful, versatile, and theoreticallygrounded un...
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Functional Bayesian Filter
We present a general nonlinear Bayesian filter for highdimensional stat...
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Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image
To fully understand interactions between marine hydrokinetic (MHK) equip...
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Multiscale Principle of Relevant Information for Hyperspectral Image Classification
This paper proposes a novel architecture, termed multiscale principle of...
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Minimum Error Entropy Kalman Filter
To date most linear and nonlinear Kalman filters (KFs) have been develop...
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Maximum Correntropy Criterion with Variable Center
Correntropy is a local similarity measure defined in kernel space and th...
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Reduction of Markov Chains using a ValueofInformationBased Approach
In this paper, we propose an approach to obtain reducedorder models of ...
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An Exact Reformulation of FeatureVectorbased RadialBasisFunction Networks for Graphbased Observations
Radialbasisfunction networks are traditionally defined for sets of vec...
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Theory and Algorithms for Pulse Signal Processing
The integrate and fire converter transforms an analog signal into train ...
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Simple stopping criteria for information theoretic feature selection
Information theoretic feature selection aims to select a smallest featur...
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Multivariate Extension of Matrixbased Renyi's αorder Entropy Functional
The matrixbased Renyi's αorder entropy functional was recently introdu...
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RequestandReverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels
One important assumption underlying common classification models is the ...
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Understanding Convolutional Neural Network Training with Information Theory
Using information theoretic concepts to understand and explore the inner...
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Understanding Autoencoders with Information Theoretic Concepts
Despite their great success in practical applications, there is still a ...
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Guided Policy Exploration for Markov Decision Processes using an UncertaintyBased ValueofInformation Criterion
Reinforcement learning in environments with many actionstate pairs is c...
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Augmented Space Linear Model
The linear model uses the space defined by the input to project the targ...
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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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Quantized Minimum Error Entropy Criterion
Comparing with traditional learning criteria, such as mean square error ...
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Using the Value of Information to Explore Stochastic, Discrete MultiArmed Bandits
In this paper, we propose an informationtheoretic exploration strategy ...
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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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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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Robustness of Maximum Correntropy Estimation Against Large Outliers
The maximum correntropy criterion (MCC) has recently been successfully a...
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Analysis of Agent Expertise in Ms. PacMan using ValueofInformationbased Policies
Conventional reinforcement learning methods for Markov decision processe...
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Exploiting SpatioTemporal Structure with Recurrent WinnerTakeAll Networks
We propose a convolutional recurrent neural network, with WinnerTakeAl...
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Kernel RiskSensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Nonlinear similarity measures defined in kernel space, such as correntro...
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Maximum Correntropy Kalman Filter
Traditional Kalman filter (KF) is derived under the wellknown minimum m...
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Generalized Correntropy for Robust Adaptive Filtering
As a robust nonlinear similarity measure in kernel space, correntropy ha...
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Kernel Least Mean Square with Adaptive Kernel Size
Kernel adaptive filters (KAF) are a class of powerful nonlinear filters ...
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Information Theoretic Learning with Infinitely Divisible Kernels
In this paper, we develop a framework for information theoretic learning...
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Deep Predictive Coding Networks
The quality of data representation in deep learning methods is directly ...
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Measures of Entropy from Data Using Infinitely Divisible Kernels
Information theory provides principled ways to analyze different inferen...
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Jose C. Principe
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Distinguished Professor at University of Florida Department of Electrical and Computer Engineering