Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework

• URL: http://arxiv.org/abs/2310.09952v1
• Date: Sun, 15 Oct 2023 21:07:09 GMT
• Title: Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework
• Authors: Arshia Soltani Moakhar, Mohammad Azizmalayeri, Hossein Mirzaei, Mohammad Taghi Manzuri, Mohammad Hossein Rohban
• Abstract summary: We propose a local objective for neurons that align them to exhibit similarities to error-backpropagation. We examine a neural network comprising decentralized, self-interested neurons seeking to maximize their local objective. We demonstrate the learning capacity of these multi-agent neural networks through experiments on three datasets.
• Score: 6.446189857311325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues. In contrast, error-backpropagation has demonstrated its effectiveness for training deep networks in practice. In this study, we propose a local objective for neurons that, when pursued by neurons individually, align them to exhibit similarities to error-backpropagation in terms of efficiency and scalability during training. For this purpose, we examine a neural network comprising decentralized, self-interested neurons seeking to maximize their local objective -- attention from subsequent layer neurons -- and identify the optimal strategy for neurons. We also analyze the relationship between this strategy and backpropagation, establishing conditions under which the derived strategy is equivalent to error-backpropagation. Lastly, we demonstrate the learning capacity of these multi-agent neural networks through experiments on three datasets and showcase their superior performance relative to error-backpropagation in a catastrophic forgetting benchmark.

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