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Found 2 papers, 2 papers with code
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success.
Ranked #49 on
Image Classification
on STL-10
Learning across scales - A multiscale method for Convolution Neural Networks
In this work we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs).
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