Unifying Back-Propagation and Forward-Forward Algorithms through Model Predictive Control

REN Lianhai, Qianxiao Li

Keywords: deep learning optimization, model predictive control

TL;DR: We present a novel MPC framework for deep learning, unifying BP and Forward-Forward techniques. We analyze accuracy and memory trade-offs and validate a horizon selection algorithm with theoretical and experimental support.

Abstract:

We introduce a Model Predictive Control (MPC) framework for training deep neural networks, systematically unifying the Back-Propagation (BP) and Forward-Forward (FF) algorithms. At the same time, it gives rise to a range of intermediate training algorithms with varying look-forward horizons, leading to a performance-efficiency trade-off. We perform a precise analysis of this trade-off on a deep linear network, where the qualitative conclusions carry over to general networks. Based on our analysis, we propose a principled method to choose the optimization horizon based on given objectives and model specifications. Numerical results on various models and tasks demonstrate the versatility of our method.

Primary Area: optimization

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Submission Number: 1739