2 citations · 4 across the 10 of their papers we have counts for
5 papers · 1 filter
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…