15 citations · 45 across the 15 of their papers we have counts for
6 papers · 1 filter
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms
Michael Perlmutter, Alexander Tong, Feng Gao +2
The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. Recently, several works have introduced g…
Wavelet invariants for statistically robust multi-reference alignment
Matthew Hirn, Anna Little
We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference a…
Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Nathan Brugnone, Alex Gonopolskiy, Mark W. Moyle +7
Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here…
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
Michael Perlmutter, Feng Gao, Guy Wolf +1
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks. Inspired by recent interest in geo…
Scattering Statistics of Generalized Spatial Poisson Point Processes
Michael Perlmutter, Jieqian He, Matthew Hirn
We present a machine learning model for the analysis of randomly generated discrete signals, modeled as the points of an inhomogeneous, compound Poisson point process. Like the wav…
Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction
Xavier Brumwell, Paul Sinz, Kwang Jin Kim +2
A general machine learning architecture is introduced that uses wavelet scattering coefficients of an inputted three dimensional signal as features. Solid harmonic wavelet scatteri…