Related papers: Nature-Inspired Local Propagation

Nature-Inspired Local Propagation

URL: http://arxiv.org/abs/2402.05959v1
Date: Sun, 4 Feb 2024 21:43:37 GMT
Title: Nature-Inspired Local Propagation
Authors: Alessandro Betti, Marco Gori
Abstract summary: Natural learning processes rely on mechanisms where data representation and learning are intertwined in such a way as to respect locality. We show that the algorithmic interpretation of the derived "laws of learning", which takes the structure of Hamiltonian equations, reduces to Backpropagation when the speed of propagation goes to infinity. This opens the doors to machine learning based on full on-line information that are based the replacement of Backpropagation with the proposed local algorithm.
Score: 68.63385571967267
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The spectacular results achieved in machine learning, including the recent advances in generative AI, rely on large data collections. On the opposite, intelligent processes in nature arises without the need for such collections, but simply by online processing of the environmental information. In particular, natural learning processes rely on mechanisms where data representation and learning are intertwined in such a way to respect spatiotemporal locality. This paper shows that such a feature arises from a pre-algorithmic view of learning that is inspired by related studies in Theoretical Physics. We show that the algorithmic interpretation of the derived "laws of learning", which takes the structure of Hamiltonian equations, reduces to Backpropagation when the speed of propagation goes to infinity. This opens the doors to machine learning studies based on full on-line information processing that are based the replacement of Backpropagation with the proposed spatiotemporal local algorithm.

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