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Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
The dream of machine learning in materials science is for a model to lea...
06/01/2020 ∙ by Paul Sinz, et al. ∙
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Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms
The scattering transform is a multilayered wavelet-based deep learning a...
11/14/2019 ∙ by Michael Perlmutter, et al. ∙
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Wavelet invariants for statistically robust multi-reference alignment
We propose a nonlinear, wavelet based signal representation that is tran...
09/24/2019 ∙ by Matthew Hirn, et al. ∙
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Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
The Euclidean scattering transform was introduced nearly a decade ago to...
05/24/2019 ∙ by Michael Perlmutter, et al. ∙
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Scattering Statistics of Generalized Spatial Poisson Point Processes
We present a machine learning model for the analysis of randomly generat...
02/10/2019 ∙ by Michael Perlmutter, et al. ∙
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Geometric Scattering on Manifolds
We present a mathematical model for geometric deep learning based upon a...
12/15/2018 ∙ by Michael Perlmutter, et al. ∙
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Graph Classification with Geometric Scattering
One of the most notable contributions of deep learning is the applicatio...
10/07/2018 ∙ by Feng Gao, et al. ∙
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Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
We present a machine learning algorithm for the prediction of molecule p...
05/01/2018 ∙ by Michael Eickenberg, et al. ∙
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Structural Risk Minimization for C^1,1(R^d) Regression
One means of fitting functions to high-dimensional data is by providing ...
03/29/2018 ∙ by Adam Gustafson, et al. ∙
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Wavelet Scattering Regression of Quantum Chemical Energies
We introduce multiscale invariant dictionaries to estimate quantum chemi...
05/16/2016 ∙ by Matthew Hirn, et al. ∙
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Quantum Energy Regression using Scattering Transforms
We present a novel approach to the regression of quantum mechanical ener...
02/06/2015 ∙ by Matthew Hirn, et al. ∙
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