2 citations · 4 across the 10 of their papers we have counts for
10 papers
Contrastive Entropy Bounds for Density and Conditional Density Decomposition
Bo Hu, Jose C. Principe
This paper studies the interpretability of neural network features from a Bayesian Gaussian view, where optimizing a cost is reaching a probabilistic bound; learning a model approx…
A Simple and Effective Method for Uncertainty Quantification and OOD Detection
Yaxin Ma, Benjamin Colburn, Jose C. Principe
Bayesian neural networks and deep ensemble methods have been proposed for uncertainty quantification; however, they are computationally intensive and require large storage. By util…
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy
Hongming Li, Zhao Yang, Shujian Yu +3
Reinforcement learning agents depend on reward signals whose density is rarely under the designer's control, and when such signals are absent, an agent must generate its own drive…
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
Shihan Ma, Bo Hu, Tianyu Jia +5
The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and e…
Cauchy-Schwarz Divergence Information Bottleneck for Regression
Shujian Yu, Xi Yu, Sigurd Løkse +2
The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum suf…
A Closed-Form Solution for Kernel Adaptive Filtering
Benjamin Colburn, Luis G. Sanchez Giraldo, Kan Li +1
Unlike the conventional kernel adaptive filtering (KAF) approach of using a fixed kernel to define the Reproducing Kernel Hilbert Space (RKHS), this paper embeds the statistics of…
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery
Isaac J. Sledge, Dominic M. Byrne, Jonathan L. King +6
We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a sup…
An Alternate View on Optimal Filtering in an RKHS
Benjamin Colburn, Jose C. Principe, Luis G. Sanchez Giraldo
Kernel Adaptive Filtering (KAF) are mathematically principled methods which search for a function in a Reproducing Kernel Hilbert Space. While they work well for tasks such as time…
IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction
Yuehai Chen
Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians…
Signal Processing with Pulse Trains: An Algebraic Approach- Part I
Gabriel Nallathambi, Jose Principe
Recently we have shown that it is possible to represent continuous amplitude, continuous time, band limited signals with an error as small as desired using pulse trains via the int…