Bacho, Florian, Chu, Dominique (2024) Low-variance Forward Gradients using Direct Feedback Alignment and momentum. Neural Networks, 169 . pp. 572-583. ISSN 0893-6080. (doi:10.1016/j.neunet.2023.10.051) (KAR id:104083)
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Official URL: https://doi.org/10.1016/j.neunet.2023.10.051 |
Abstract
Supervised learning in deep neural networks is commonly performed using error backpropagation. However, the sequential propagation of errors during the backward pass limits its scalability and applicability to low-powered neuromorphic hardware. Therefore, there is growing interest in finding local alternatives to backpropagation. Recently proposed methods based on forward-mode automatic differentiation suffer from high variance in large deep neural networks, which affects convergence. In this paper, we propose the Forward Direct Feedback Alignment algorithm that combines Activity-Perturbed Forward Gradients with Direct Feedback Alignment and momentum. We provide both theoretical proofs and empirical evidence that our proposed method achieves lower variance than forward gradient techniques. In this way, our approach enables faster convergence and better performance when compared to other local alternatives to backpropagation and opens a new perspective for the development of online learning algorithms compatible with neuromorphic systems.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.neunet.2023.10.051 |
Uncontrolled keywords: | Backpropagation, Forward Gradient, Gradient estimates, Direct Feedback Alignment, Low variance |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 15 Mar 2024 15:14 UTC |
Last Modified: | 18 Mar 2024 11:59 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/104083 (The current URI for this page, for reference purposes) |
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