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arXiv · 2204.04718

Rethinking Exponential Averaging of the Fisher

Constantin Octavian Puiu
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Abstract

In optimization for Machine learning (ML), it is typical that curvature-matrix (CM) estimates rely on an exponential average (EA) of local estimates (giving EA-CM algorithms). This approach has little principled justification, but is very often used in practice. In this paper, we draw a connection between EA-CM algorithms and what we call a "Wake of Quadratic regularized models". The outlined connection allows us to understand what EA-CM algorithms are doing from an optimization perspective. Generalizing from the established connection, we propose a new family of algorithms, "KL-Divergence Wake-Regularized Models" (KLD-WRM). We give three different practical instantiations of KLD-WRM, and show numerically that these outperform K-FAC on MNIST.

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Constantin Octavian Puiu. 2022-06-30. Rethinking Exponential Averaging of the Fisher. https://arxiv.org/abs/2204.04718

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