First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models

• URL: http://arxiv.org/abs/2502.04121v2
• Date: Thu, 13 Mar 2025 18:41:50 GMT
• Title: First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
• Authors: Sagi Meir, Tommer D. Keidar, Shlomi Reuveni, Barak Hirshberg,
• Abstract summary: Several protocols have been developed to improve the training of machine learning models.<n>We frame them as first-passage processes and consider their response to perturbations.<n>We show that if the unperturbed learning process reaches a quasi-steady state, the response at a single perturbation frequency can predict the behavior at a wide range of timescales.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models have become indispensable tools in applications across the physical sciences. Their training is often time-consuming, vastly exceeding the inference timescales. Several protocols have been developed to perturb the learning process and improve the training, such as shrink and perturb, warm restarts, and stochastic resetting. For classifiers, these perturbations have been shown to result in enhanced speedups or improved generalization. However, the design of such perturbations is usually done ad hoc by intuition and trial and error. To rationally optimize training protocols, we frame them as first-passage processes and consider their response to perturbations. We show that if the unperturbed learning process reaches a quasi-steady state, the response at a single perturbation frequency can predict the behavior at a wide range of frequencies. We employ this approach to a CIFAR-10 classifier using the ResNet-18 model and identify a useful perturbation and frequency among several possibilities. Our work allows optimization of perturbations for improving the training of machine learning models using a first-passage approach.

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