3 papers
cs.LG2025
A Family of Kernelized Matrix Costs for Multiple-Output Mixture Neural Networks
Bo Hu, José C. Príncipe
Pairwise distance-based costs are crucial for self-supervised and contrastive feature learning. Mixture Density Networks (MDNs) are a widely used approach for generative models and…
cs.LG2024
Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical Systems
Kan Li, José C. Príncipe
Motivated by the surge of interest in Koopman operator theory, we propose a machine-learning alternative based on a functional Bayesian perspective for operator-theoretic modeling…
cs.CV2023
Feature Learning in Image Hierarchies using Functional Maximal Correlation
Bo Hu, Yuheng Bu, José C. Príncipe
This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in…