Frame based Kernel Methods for Automatic Classification in Hyperspectral Data. - John J. Benedetto; Wojciech Czaja; Justin Flake; Matthew J. Hirn (IGARSS (4), 2009)

Sparse endmember extraction and demixing. - Martin Ehler; Matthew J. Hirn (IGARSS, 2012)

Bi-stochastic kernels via asymmetric affinity functions - Ronald R. Coifman; Matthew J. Hirn (CoRR, 2012)

Diffusion maps for changing data - Ronald R. Coifman; Matthew J. Hirn (CoRR, 2012)

Computing minimal interpolants in C1, 1(ℝd). - Ariel Herbert-Voss; Matthew J. Hirn; Frederick McCollum (CoRR, 2014)

Frames for subspaces of $ mathbb{C}^N$. - Matthew J. Hirn; David P. Widemann (CoRR, 2014)

Quantum Energy Regression using Scattering Transforms. - Matthew J. Hirn; Nicolas Poilvert; Stéphane Mallat (CoRR, 2015)

Time Coupled Diffusion Maps. - Nicholas F. Marshall; Matthew J. Hirn (CoRR, 2016)

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities. - Michael Eickenberg; Georgios Exarchakis; Matthew J. Hirn; Stéphane Mallat (NIPS, 2017)

Wavelet Scattering Regression of Quantum Chemical Energies. - Matthew J. Hirn; Stéphane Mallat; Nicolas Poilvert (Multiscale Model. Simul., 2017)

Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties. - Michael Eickenberg; Georgios Exarchakis; Matthew J. Hirn; Stéphane Mallat; Louis Thiry (CoRR, 2018)

Graph Classification with Geometric Scattering. - Feng Gao; Guy Wolf; Matthew J. Hirn (CoRR, 2018)

Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction. - Xavier Brumwell; Paul Sinz; Kwang Jin Kim; Yue Qi; Matthew J. Hirn (CoRR, 2018)

Geometric Scattering on Manifolds. - Michael Perlmutter; Guy Wolf; Matthew J. Hirn (CoRR, 2018)

Kymatio: Scattering Transforms in Python. - Mathieu Andreux; Tomás Angles; Georgios Exarchakis; Roberto Leonarduzzi; Gaspar Rochette; Louis Thiry; John Zarka; Stéphane Mallat; Joakim Andén; Eugene Belilovsky; Joan Bruna; Vincent Lostanlen; et al. (CoRR, 2018)

Coarse Graining of Data via Inhomogeneous Diffusion Condensation. - Nathan Brugnone; Smita Krishnaswamy; Alex Gonopolskiy; Mark W. Moyle; Manik Kuchroo; David van Dijk; Kevin R. Moon; Daniel Colón-Ramos; Guy Wolf; Matthew J. Hirn (IEEE BigData, 2019)

Geometric Scattering for Graph Data Analysis. - Feng Gao; Guy Wolf; Matthew J. Hirn (ICML, 2019)

Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms. - Michael Perlmutter; Feng Gao; Guy Wolf; Matthew J. Hirn (CoRR, 2019)

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds. - Michael Perlmutter; Feng Gao; Guy Wolf; Matthew J. Hirn (MSML, 2020)

Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties. - Paul Sinz; Michael W. Swift; Xavier Brumwell; Jialin Liu; Kwang Jin Kim; Yue Qi; Matthew J. Hirn (CoRR, 2020)

Kymatio: Scattering Transforms in Python. - Mathieu Andreux; Tomás Angles; Georgios Exarchakis; Roberto Leonarduzzi; Gaspar Rochette; Louis Thiry; John Zarka; Stéphane Mallat; Joakim Andén; Eugene Belilovsky; Joan Bruna; Vincent Lostanlen; et al. (J. Mach. Learn. Res., 2020)

A Hybrid Scattering Transform for Signals with Isolated Singularities. - Michael Perlmutter; Jieqian He; Mark A. Iwen; Matthew J. Hirn (ACSCC, 2021)

MagNet: A Neural Network for Directed Graphs. - Xitong Zhang; Yixuan He; Nathan Brugnone; Michael Perlmutter; Matthew J. Hirn (NeurIPS, 2021)

MagNet: A Magnetic Neural Network for Directed Graphs. - Xitong Zhang; Nathan Brugnone; Michael Perlmutter; Matthew J. Hirn (CoRR, 2021)

Texture synthesis via projection onto multiscale, multilayer statistics. - Jieqian He; Matthew J. Hirn (CoRR, 2021)

Unbiasing Procedures for Scale-invariant Multi-reference Alignment. - Matthew J. Hirn; Anna Little (CoRR, 2021)

Towards a Taxonomy of Graph Learning Datasets. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2021)

ClassicalGSG: Prediction of logP using classical molecular force fields and geometric scattering for graphs. - Nazanin Donyapour; Matthew J. Hirn; Alex Dickson (J. Comput. Chem., 2021)

Scattering Statistics of Generalized Spatial Poisson Point Processes. - Michael Perlmutter; Jieqian He; Matthew J. Hirn (ICASSP, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Sarah McGuire; Xinyi Wang; Anna Little; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; Ladislav Rampásek (LoG, 2022)

The Manifold Scattering Transform for High-Dimensional Point Cloud Data. - Joyce A. Chew; Holly R. Steach; Siddharth Viswanath; Hau-Tieng Wu; Matthew J. Hirn; Deanna Needell; Matthew D. Vesely; Smita Krishnaswamy; Michael Perlmutter (TAG-ML, 2022)

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks. - Frederik Wenkel; Yimeng Min; Matthew J. Hirn; Michael Perlmutter; Guy Wolf (CoRR, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2022)

The Manifold Scattering Transform for High-Dimensional Point Cloud Data. - Joyce A. Chew; Holly R. Steach; Siddharth Viswanath; Hau-Tieng Wu; Matthew J. Hirn; Deanna Needell; Smita Krishnaswamy; Michael Perlmutter (CoRR, 2022)

Geometric Scattering on Measure Spaces. - Joyce A. Chew; Matthew J. Hirn; Smita Krishnaswamy; Deanna Needell; Michael Perlmutter; Holly R. Steach; Siddharth Viswanath; Hau-Tieng Wu (CoRR, 2022)

NervePool: A Simplicial Pooling Layer. - Sarah McGuire; Elizabeth Munch; Matthew J. Hirn (CoRR, 2023)

Accurately modeling biased random walks on weighted networks using node2vec+. - Renming Liu; Matthew J. Hirn; Arjun Krishnan (Bioinform., 2023)

Time-Inhomogeneous Diffusion Geometry and Topology. - Guillaume Huguet; Alexander Tong 001; Bastian Rieck; Jessie Huang; Manik Kuchroo; Matthew J. Hirn; Guy Wolf; Smita Krishnaswamy (SIAM J. Math. Data Sci., 2023)

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms. - Michael Perlmutter; Alexander Tong 001; Feng Gao; Guy Wolf; Matthew J. Hirn (SIAM J. Math. Data Sci., 2023)

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. - Holly R. Steach; Siddharth Viswanath; Yixuan He; Xitong Zhang; Natalia Ivanova; Matthew J. Hirn; Michael Perlmutter; Smita Krishnaswamy (RECOMB, 2024)

Bispectrum Unbiasing for Dilation-Invariant Multi-Reference Alignment. - Liping Yin; Anna Little; Matthew J. Hirn (IEEE Trans. Signal Process., 2024)