Unifying back-propagation and forward-forward algorithms through model predictive control

• URL: http://arxiv.org/abs/2409.19561v1
• Date: Sun, 29 Sep 2024 05:35:39 GMT
• Title: Unifying back-propagation and forward-forward algorithms through model predictive control
• Authors: Lianhai Ren, Qianxiao Li,
• Abstract summary: We introduce a Model Predictive Control framework for training deep neural networks. At the same time, it gives rise to a range of intermediate training algorithms with varying look-forward horizons. We perform a precise analysis of this trade-off on a deep linear network.
• Score: 12.707050104493218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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