15 citations · 16 across the 3 of their papers we have counts for
8 papers
Quantized Gromov-Wasserstein
Samir Chowdhury, David Miller, Tom Needham
The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to allow for the comparison of probability distributions defined on different metric spaces. Scalable comp…
Path homology and temporal networks
Samir Chowdhury, Steve Huntsman, Matvey Yutin
We present an algorithm to compute path homology for simple digraphs, and use it to topologically analyze various small digraphs en route to an analysis of complex temporal network…
Generalized Spectral Clustering via Gromov-Wasserstein Learning
Samir Chowdhury, Tom Needham
We establish a bridge between spectral clustering and Gromov-Wasserstein Learning (GWL), a recent optimal transport-based approach to graph partitioning. This connection both expla…
Path homologies of deep feedforward networks
Samir Chowdhury, Thomas Gebhart, Steve Huntsman +1
We provide a characterization of two types of directed homology for fully-connected, feedforward neural network architectures. These exact characterizations of the directed homolog…
Gromov-Wasserstein Averaging in a Riemannian Framework
Samir Chowdhury, Tom Needham
We introduce a theoretical framework for performing statistical tasks---including, but not limited to, averaging and principal component analysis---on the space of (possibly asymme…
Geodesics in persistence diagram space
Samir Chowdhury
It is known that for a variety of choices of metrics, including the standard bottleneck distance, the space of persistence diagrams admits geodesics. Typically these existence resu…