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Physical Deep Learning with Biologically Plausible Training Method
[article]
2022
arXiv
pre-print
The ever-growing demand for further advances in artificial intelligence motivated research on unconventional computation based on analog physical devices. While such computation devices mimic brain-inspired analog information processing, learning procedures still relies on methods optimized for digital processing such as backpropagation. Here, we present physical deep learning by extending a biologically plausible training algorithm called direct feedback alignment. As the proposed method is
arXiv:2204.13991v1
fatcat:2nhmpjz4w5cbvg7yx57dh64edm