10 citations · 13 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 3 cited
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
John Fischer, Marko Orescanin, Justin Loomis +1
Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communi…
cs.LG2024★ 10 cited
VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern Recognition
John Fischer, Marko Orescanin, Eric Eckstrand
Transfer learning (TL) is an increasingly popular approach to training deep learning (DL) models that leverages the knowledge gained by training a foundation model on diverse, larg…