Sequential Learning Of Neural Networks for Prequential MDL

• URL: http://arxiv.org/abs/2210.07931v1
• Date: Fri, 14 Oct 2022 16:30:23 GMT
• Title: Sequential Learning Of Neural Networks for Prequential MDL
• Authors: Jorg Bornschein and Yazhe Li and Marcus Hutter
• Abstract summary: We evaluate approaches for computing prequential description lengths for image classification datasets with neural networks. Considering the computational cost, we find that online-learning with rehearsal has favorable performance. We present description lengths for a suite of image classification datasets that improve upon previously reported results by large margins.
• Score: 18.475866691786695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Minimum Description Length (MDL) provides a framework and an objective for principled model evaluation. It formalizes Occam's Razor and can be applied to data from non-stationary sources. In the prequential formulation of MDL, the objective is to minimize the cumulative next-step log-loss when sequentially going through the data and using previous observations for parameter estimation. It thus closely resembles a continual- or online-learning problem. In this study, we evaluate approaches for computing prequential description lengths for image classification datasets with neural networks. Considering the computational cost, we find that online-learning with rehearsal has favorable performance compared to the previously widely used block-wise estimation. We propose forward-calibration to better align the models predictions with the empirical observations and introduce replay-streams, a minibatch incremental training technique to efficiently implement approximate random replay while avoiding large in-memory replay buffers. As a result, we present description lengths for a suite of image classification datasets that improve upon previously reported results by large margins.

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