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cs.LG2024★ 1 cited
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
A number of popular transfer learning methods rely on grid search to select regularization hyperparameters that control over-fitting. This grid search requirement has several key d…
cs.LG2024
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
We pursue transfer learning to improve classifier accuracy on a target task with few labeled examples available for training. Recent work suggests that using a source task to learn…