2 citations · 3 across the 8 of their papers we have counts for
7 papers · 1 filter
Kernel distance measures for time series, random fields and other structured data
Srinjoy Das, Hrushikesh Mhaskar, Alexander Cloninger
This paper introduces kdiff, a novel kernel-based measure for estimating distances between instances of time series, random fields and other forms of structured data. This measure…
A deep network construction that adapts to intrinsic dimensionality beyond the domain
Alexander Cloninger, Timo Klock
We study the approximation of two-layer compositions via deep networks with ReLU activation, where is a geometrically intuitive, dimensionality reducing featur…
Nonclosedness of Sets of Neural Networks in Sobolev Spaces
Scott Mahan, Emily King, Alex Cloninger
We examine the closedness of sets of realized neural networks of a fixed architecture in Sobolev spaces. For an exactly -times differentiable activation function , we constru…
Bounding the Error From Reference Set Kernel Maximum Mean Discrepancy
Alexander Cloninger
In this paper, we bound the error induced by using a weighted skeletonization of two data sets for computing a two sample test with kernel maximum mean discrepancy. The error is qu…
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…
People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs
Alexander Cloninger, Brita Roy, Carley Riley +1
We address the problem of defining a network graph on a large collection of classes. Each class is comprised of a collection of data points, sampled in a non i.i.d. way, from some…