Matthew J. Hirn

According to our database, Matthew J. Hirn authored at least 40 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bispectrum Unbiasing for Dilation-Invariant Multi-reference Alignment.

Liping Yin, Anna Little, Matthew J. Hirn

CoRR, 2024

2023

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms.

Michael Perlmutter, Alexander Tong, Feng Gao, Guy Wolf, Matthew J. Hirn

SIAM J. Math. Data Sci., December, 2023

Time-Inhomogeneous Diffusion Geometry and Topology.

Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy

SIAM J. Math. Data Sci., June, 2023

Accurately modeling biased random walks on weighted networks using <i>node2vec+</i>.

Renming Liu, Matthew J. Hirn, Arjun Krishnan

Bioinform., January, 2023

NervePool: A Simplicial Pooling Layer.

Sarah McGuire, Elizabeth Munch, Matthew J. Hirn

CoRR, 2023

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

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

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, Ladislav Rampásek

CoRR, 2022

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks.

Frederik Wenkel, Yimeng Min, Matthew J. Hirn, Michael Perlmutter, Guy Wolf

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, Matthew D. Vesely, Smita Krishnaswamy, Michael Perlmutter

Proceedings of the Topological, 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

Proceedings of the Learning on Graphs Conference, 2022

Scattering Statistics of Generalized Spatial Poisson Point Processes.

Michael Perlmutter, Jieqian He, Matthew J. Hirn

Proceedings of the IEEE International Conference on Acoustics, 2022

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

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, Ladislav Rampásek

CoRR, 2021

Unbiasing Procedures for Scale-invariant Multi-reference Alignment.

Matthew J. Hirn, Anna Little

CoRR, 2021

Texture synthesis via projection onto multiscale, multilayer statistics.

Jieqian He, Matthew J. Hirn

CoRR, 2021

MagNet: A Magnetic Neural Network for Directed Graphs.

Xitong Zhang, Nathan Brugnone, Michael Perlmutter, Matthew J. Hirn

CoRR, 2021

MagNet: A Neural Network for Directed Graphs.

Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, Matthew J. Hirn

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Hybrid Scattering Transform for Signals with Isolated Singularities.

Michael Perlmutter, Jieqian He, Mark A. Iwen, Matthew J. Hirn

Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

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, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg

J. Mach. Learn. Res., 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

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds.

Michael Perlmutter, Feng Gao, Guy Wolf, Matthew J. Hirn

Proceedings of Mathematical and Scientific Machine Learning, 2020

2019

Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms.

Michael Perlmutter, Feng Gao, Guy Wolf, Matthew J. Hirn

CoRR, 2019

Geometric Scattering for Graph Data Analysis.

Feng Gao, Guy Wolf, Matthew J. Hirn

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

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, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg

CoRR, 2018

Geometric Scattering on Manifolds.

Michael Perlmutter, 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

Graph Classification with Geometric Scattering.

Feng Gao, Guy Wolf, Matthew J. Hirn

CoRR, 2018

Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties.

Michael Eickenberg, Georgios Exarchakis, Matthew J. Hirn, Stéphane Mallat, Louis Thiry

CoRR, 2018

2017

Wavelet Scattering Regression of Quantum Chemical Energies.

Matthew J. Hirn, Stéphane Mallat, Nicolas Poilvert

Multiscale Model. Simul., 2017

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities.

Michael Eickenberg, Georgios Exarchakis, Matthew J. Hirn, Stéphane Mallat

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Time Coupled Diffusion Maps.

Nicholas F. Marshall, Matthew J. Hirn

CoRR, 2016

2015

Quantum Energy Regression using Scattering Transforms.

Matthew J. Hirn, Nicolas Poilvert, Stéphane Mallat

CoRR, 2015

2014

Frames for subspaces of $\mathbb{C}^N$.

Matthew J. Hirn, David P. Widemann

CoRR, 2014

Computing minimal interpolants in $C^{1, 1}(\mathbb{R}^d)$.

Ariel Herbert-Voss, Matthew J. Hirn, Frederick McCollum

CoRR, 2014

2012

Diffusion maps for changing data

Ronald R. Coifman, Matthew J. Hirn

CoRR, 2012

Bi-stochastic kernels via asymmetric affinity functions

Ronald R. Coifman, Matthew J. Hirn

CoRR, 2012

Sparse endmember extraction and demixing.

Martin Ehler, Matthew J. Hirn

Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012

2009

Frame based Kernel Methods for Automatic Classification in Hyperspectral Data.

John J. Benedetto, Wojciech Czaja, Justin Flake, Matthew J. Hirn

Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2009