Developing Constrained Neural Units Over Time

1 Sep 2020Alessandro BettiMarco GoriSimone MarulloStefano Melacci

In this paper we present a foundational study on a constrained method that defines learning problems with Neural Networks in the context of the principle of least cognitive action, which very much resembles the principle of least action in mechanics. Starting from a general approach to enforce constraints into the dynamical laws of learning, this work focuses on an alternative way of defining Neural Networks, that is different from the majority of existing approaches... (read more)

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