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cs.LG2024
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation
Louis L. Chen, Bobbie Chern, Eric Eckstrand +2
Labeling errors in datasets are common, arising in a variety of contexts, such as human labeling, noisy labeling, and weak labeling (i.e., image classification). Although neural ne…
cs.LG2024
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…