780 citations · 1.8k across the 76 of their papers we have counts for
15 papers · 1 filter
Information Theoretic Structured Generative Modeling
Bo Hu, Shujian Yu, Jose C. Principe
Rényi's information provides a theoretical foundation for tractable and data-efficient non-parametric density estimation, based on pair-wise evaluations in a reproducing kernel Hil…
Estimating Rényi's -Cross-Entropies in a Matrix-Based Way
Isaac J. Sledge, Jose C. Principe
Conventional information-theoretic quantities assume access to probability distributions. Estimating such distributions is not trivial. Here, we consider function-based formulation…
Analysis of Intra-Operative Physiological Responses Through Complex Higher-Order SVD for Long-Term Post-Operative Pain Prediction
Raheleh Baharloo, Jose C. Principe, Parisa Rashidi +1
Long-term pain conditions after surgery and patients' responses to pain relief medications are not yet fully understood. While recent studies developed an index for nociception lev…
A Physics inspired Functional Operator for Model Uncertainty Quantification in the RKHS
Rishabh Singh, Jose C. Principe
Accurate uncertainty quantification of model predictions is a crucial problem in machine learning. Existing Bayesian methods, being highly iterative, are expensive to implement and…
Uncertainty quantification for multiclass data description
Leila Kalantari, Jose Principe, Kathryn E. Sieving
In this manuscript, we propose a multiclass data description model based on kernel Mahalanobis distance (MDD-KM) with self-adapting hyperparameter setting. MDD-KM provides uncertai…
External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery
Isaac J. Sledge, Christopher D. Toole, Joseph A. Maestri +1
We propose a memory-based framework for real-time, data-efficient target analysis in forward-looking-sonar (FLS) imagery. Our framework relies on first removing non-discriminative…
An Information-Theoretic Approach for Automatically Determining the Number of States when Aggregating Markov Chains
Isaac J. Sledge, Jose C. Principe
A fundamental problem when aggregating Markov chains is the specification of the number of state groups. Too few state groups may fail to sufficiently capture the pertinent dynamic…
Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
Shiyu Duan, Spencer Chang, Jose C. Principe
In supervised learning, obtaining a large set of fully-labeled training data is expensive. We show that we do not always need full label information on every single training exampl…
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts
Rishabh Singh, Jose C. Principe
Traditional Bayesian approaches for model uncertainty quantification rely on notoriously difficult processes of marginalization over each network parameter to estimate its probabil…
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning
Isaac J. Sledge, Darshan W. Bryner, Jose C. Principe
Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of…
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional
Xi Yu, Shujian Yu, Jose C. Principe
We introduce the matrix-based Renyi's -order entropy functional to parameterize Tishby et al. information bottleneck (IB) principle with a neural network. We term our methodolog…
Measuring Dependence with Matrix-based Entropy Functional
Shujian Yu, Francesco Alesiani, Xi Yu +2
Measuring the dependence of data plays a central role in statistics and machine learning. In this work, we summarize and generalize the main idea of existing information-theoretic…
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations
Isaac J. Sledge, Jose C. Principe
Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feed-back connections to modulate latent feature representations of stimu…
Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders
Isaac J. Sledge, Matthew S. Emigh, Jonathan L. King +3
We propose a framework for saliency-based, multi-target detection and segmentation of circular-scan, synthetic-aperture-sonar (CSAS) imagery. Our framework relies on a multi-branch…
Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods
Shiyu Duan, Jose C. Principe
This tutorial paper surveys provably optimal alternatives to end-to-end backpropagation (E2EBP) -- the de facto standard for training deep architectures. Modular training refers to…