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cs.LG2022
Dissipative residual layers for unsupervised implicit parameterization of data manifolds
Viktor Reshniak
We propose an unsupervised technique for implicit parameterization of data manifolds. In our approach, the data is assumed to belong to a lower dimensional manifold in a higher dim…
cs.LG2019
Robust learning with implicit residual networks
Viktor Reshniak, Clayton Webster
In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outp…