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cs.LG2025
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…
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…