Differentiable Implicit Layers
Machine Learning
2020-11-17 v2 Neural and Evolutionary Computing
Abstract
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optionally depend on some input; making them perfectly suitable as a learnable layer in a neural network. We demonstrate our scheme on different applications: (i) neural ODEs with the implicit Euler method, and (ii) system identification in model predictive control.
Cite
@article{arxiv.2010.07078,
title = {Differentiable Implicit Layers},
author = {Andreas Look and Simona Doneva and Melih Kandemir and Rainer Gemulla and Jan Peters},
journal= {arXiv preprint arXiv:2010.07078},
year = {2020}
}