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Towards a Taxonomy of Graph Learning Datasets


Oct 27, 2021
Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

* in Data-Centric AI Workshop at NeurIPS 2021 

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A Hybrid Scattering Transform for Signals with Isolated Singularities


Oct 10, 2021
Michael Perlmutter, Jieqian He, Mark Iwen, Matthew Hirn


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Accurately Modeling Biased Random Walks on Weighted Graphs Using $\textit{Node2vec+}$


Sep 15, 2021
Renming Liu, Matthew Hirn, Arjun Krishnan


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Unbiasing Procedures for Scale-invariant Multi-reference Alignment


Jul 02, 2021
Matthew Hirn, Anna Little

* 12 pages, 5 figures. Code reproducing numerical results at https://bitbucket.org/annavlittle/inversion-unbiasing/src/master/ 

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Texture synthesis via projection onto multiscale, multilayer statistics


May 22, 2021
Jieqian He, Matthew Hirn

* 14 pages, 16 figures 

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MagNet: A Magnetic Neural Network for Directed Graphs


Feb 22, 2021
Xitong Zhang, Nathan Brugnone, Michael Perlmutter, Matthew Hirn

* 14 pages, 4 figures, 8 tables 

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Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties


Jun 01, 2020
Paul Sinz, Michael W. Swift, Xavier Brumwell, Jialin Liu, Kwang Jin Kim, Yue Qi, Matthew Hirn

* 15 pages; 10 figures; 4 tables 

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Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms


Nov 14, 2019
Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn


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Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds


May 24, 2019
Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn


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Scattering Statistics of Generalized Spatial Poisson Point Processes


Feb 10, 2019
Michael Perlmutter, Jieqian He, Matthew Hirn


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Geometric Scattering on Manifolds


Dec 19, 2018
Michael Perlmutter, Guy Wolf, Matthew Hirn

* A shorter version of this paper appeared in the NeurIPS 2018 Integration of Deep Learning Theories Workshop, Montr\'eal, Canada 

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Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction


Nov 21, 2018
Xavier Brumwell, Paul Sinz, Kwang Jin Kim, Yue Qi, Matthew Hirn

* NIPS 2018 Workshop on Machine Learning for Molecules and Materials, Montreal, Canada 

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Graph Classification with Geometric Scattering


Oct 07, 2018
Feng Gao, Guy Wolf, Matthew Hirn


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Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties


May 01, 2018
Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stéphane Mallat, Louis Thiry

* Keywords: wavelets, electronic structure calculations, solid harmonics, invariants, multilinear regression 

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Structural Risk Minimization for $C^{1,1}(\mathbb{R}^d)$ Regression


Mar 30, 2018
Adam Gustafson, Matthew Hirn, Kitty Mohammed, Hariharan Narayanan, Jason Xu

* 32 pages, 3 figures 

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Wavelet Scattering Regression of Quantum Chemical Energies


Jan 06, 2017
Matthew Hirn, Stéphane Mallat, Nicolas Poilvert

* Multiscale Modeling and Simulation, volume 15, issue 2, 827-863, 2017 
* Replaces arXiv:1502.02077. v2: Minor clarifications, additions, and typo corrections. v3: Minor edits. Software to reproduce the numerical results is available at: https://github.com/matthew-hirn/ScatNet-QM-2D 

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Quantum Energy Regression using Scattering Transforms


May 20, 2016
Matthew Hirn, Nicolas Poilvert, Stéphane Mallat

* 9 pages, 2 figures, 1 table. v2: Correction to Section 4.3. v3: Replaced by arXiv:1605.04654 

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