2.1k citations · 2.7k across the 14 of their papers we have counts for
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stat.ML2015
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks
Uri Shaham, Roy Lederman
We consider the statistical problem of learning common source of variability in data which are synchronously captured by multiple sensors, and demonstrate that Siamese neural netwo…
stat.ML2015★ 204 cited
Provable approximation properties for deep neural networks
Uri Shaham, Alexander Cloninger, Ronald R. Coifman
We discuss approximation of functions using deep neural nets. Given a function on a -dimensional manifold , we construct a sparsely-connected depth-4…
stat.ML2015
Diffusion Nets
Gal Mishne, Uri Shaham, Alexander Cloninger +1
Non-linear manifold learning enables high-dimensional data analysis, but requires out-of-sample-extension methods to process new data points. In this paper, we propose a manifold l…