G-TRACER: Expected Sharpness Optimization

• URL: http://arxiv.org/abs/2306.13914v1
• Date: Sat, 24 Jun 2023 09:28:49 GMT
• Title: G-TRACER: Expected Sharpness Optimization
• Authors: John Williams, Stephen Roberts
• Abstract summary: G-TRACER promotes generalization by seeking flat minima, and has a sound theoretical basis as an approximation to a natural-gradient descent based optimization of a generalized Bayes objective. We show that the method converges to a neighborhood of a local minimum of the unregularized objective, and demonstrate competitive performance on a number of benchmark computer vision and NLP datasets.
• Score: 1.2183405753834562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new regularization scheme for the optimization of deep learning architectures, G-TRACER ("Geometric TRACE Ratio"), which promotes generalization by seeking flat minima, and has a sound theoretical basis as an approximation to a natural-gradient descent based optimization of a generalized Bayes objective. By augmenting the loss function with a TRACER, curvature-regularized optimizers (eg SGD-TRACER and Adam-TRACER) are simple to implement as modifications to existing optimizers and don't require extensive tuning. We show that the method converges to a neighborhood (depending on the regularization strength) of a local minimum of the unregularized objective, and demonstrate competitive performance on a number of benchmark computer vision and NLP datasets, with a particular focus on challenging low signal-to-noise ratio problems.

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