Neural Networks with Cheap Differential Operators
Ricky T. Q. Chen, David Duvenaud
University of Toronto, Vector Institute
{rtqichen,duvenaud}@cs.toronto.edu
Abstract
Gradients of neural networks can be computed efficiently for any architecture,
but some applications require computing differential operators with higher time
complexity. We describe a family of neural network architectures that allow easy
acce ...


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