4 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…
Solving the BGK Model and Boltzmann equation by Fourier Neural Operator with conservative constraints
Boyun Hu, Kunlun Qi
The numerical approximation of the Boltzmann collision operator presents significant challenges arising from its high dimensionality, nonlinear structure, and nonlocal integral for…
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…
MGFF-TDNN: A Multi-Granularity Feature Fusion TDNN Model with Depth-Wise Separable Module for Speaker Verification
Ya Li, Bin Zhou, Bo Hu
In speaker verification, traditional models often emphasize modeling long-term contextual features to capture global speaker characteristics. However, this approach can neglect fin…