15 citations · 15 across the 4 of their papers we have counts for
4 papers · 1 filter
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky +1
Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance f…
Defending against Adversarial Images using Basis Functions Transformations
Uri Shaham, James Garritano, Yutaro Yamada +5
We study the effectiveness of various approaches that defend against adversarial attacks on deep networks via manipulations based on basis function representations of images. Speci…
Stochastic Neighbor Embedding separates well-separated clusters
Uri Shaham, Stefan Steinerberger
Stochastic Neighbor Embedding and its variants are widely used dimensionality reduction techniques -- despite their popularity, no theoretical results are known. We prove that the…
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…