G-TRACER: Expected Sharpness Optimization

John Williams; Stephen Roberts

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arXiv · 2306.13914

G-TRACER: Expected Sharpness Optimization

John Williams·Stephen Roberts

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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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John Williams, Stephen Roberts. 2023-06-24. G-TRACER: Expected Sharpness Optimization. https://arxiv.org/abs/2306.13914

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