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The least-control principle for local learning at equilibrium
[article]
2022
arXiv
pre-print
Equilibrium systems are a powerful way to express neural computations. As special cases, they include models of great current interest in both neuroscience and machine learning, such as deep neural networks, equilibrium recurrent neural networks, deep equilibrium models, or meta-learning. Here, we present a new principle for learning such systems with a temporally- and spatially-local rule. Our principle casts learning as a least-control problem, where we first introduce an optimal controller
arXiv:2207.01332v2
fatcat:kvyrekhvt5avdma2lnoyei6jla