arXiv Machine Learning By Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee

Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments

Read the original on arXiv Machine Learning →

arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.

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arXiv AI
Jul 29

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

arXiv:2607. 25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function.

By Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch, Markus Goetz, Achim Streit, Sebastian Krumscheid, Charlotte Debus