Matthew J. Hirn

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 34

Top Topics

Top Venues

SIAM J. Math. Data Sci.

Publications

Holly R. Steach, Siddharth Viswanath, Yixuan He, Xitong Zhang, Natalia Ivanova, Matthew J. Hirn, Michael Perlmutter, Smita Krishnaswamy
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. RECOMB (2024)
Liping Yin, Anna Little, Matthew J. Hirn
Bispectrum Unbiasing for Dilation-Invariant Multi-reference Alignment. CoRR (2024)
Michael Perlmutter, Alexander Tong, Feng Gao, Guy Wolf, Matthew J. Hirn
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms. SIAM J. Math. Data Sci. 5 (4) (2023)
Sarah McGuire, Elizabeth Munch, Matthew J. Hirn
NervePool: A Simplicial Pooling Layer. CoRR (2023)
Renming Liu, Matthew J. Hirn, Arjun Krishnan
. Bioinform. 39 (1) (2023)
Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy
Time-Inhomogeneous Diffusion Geometry and Topology. SIAM J. Math. Data Sci. 5 (2) (2023)
Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy
Time-inhomogeneous diffusion geometry and topology. CoRR (2022)
Joyce A. Chew, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu, Matthew J. Hirn, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter
The Manifold Scattering Transform for High-Dimensional Point Cloud Data. CoRR (2022)
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
Taxonomy of Benchmarks in Graph Representation Learning. CoRR (2022)
Joyce Chew, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu, Matthew J. Hirn, Deanna Needell, Matthew D. Vesely, Smita Krishnaswamy, Michael Perlmutter
The Manifold Scattering Transform for High-Dimensional Point Cloud Data. TAG-ML (2022)
Joyce Chew, Matthew J. Hirn, Smita Krishnaswamy, Deanna Needell, Michael Perlmutter, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu
Geometric Scattering on Measure Spaces. CoRR (2022)
Michael Perlmutter, Jieqian He, Matthew J. Hirn
Scattering Statistics of Generalized Spatial Poisson Point Processes. ICASSP (2022)
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
Taxonomy of Benchmarks in Graph Representation Learning. LoG (2022)
Xitong Zhang, Nathan Brugnone, Michael Perlmutter, Matthew J. Hirn
MagNet: A Magnetic Neural Network for Directed Graphs. CoRR (2021)
Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, Matthew J. Hirn
MagNet: A Neural Network for Directed Graphs. NeurIPS (2021)
Jieqian He, Matthew J. Hirn
Texture synthesis via projection onto multiscale, multilayer statistics. CoRR (2021)
Matthew J. Hirn, Anna Little
Unbiasing Procedures for Scale-invariant Multi-reference Alignment. CoRR (2021)
Nazanin Donyapour, Matthew J. Hirn, Alex Dickson
ClassicalGSG: Prediction of logP using classical molecular force fields and geometric scattering for graphs. J. Comput. Chem. 42 (14) (2021)
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
Kymatio: Scattering Transforms in Python. J. Mach. Learn. Res. 21 (2020)
Michael Perlmutter, Feng Gao, Guy Wolf, Matthew J. Hirn
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds. MSML (2020)
Paul Sinz, Michael W. Swift, Xavier Brumwell, Jialin Liu, Kwang Jin Kim, Yue Qi, Matthew J. Hirn
Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties. CoRR (2020)
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
Coarse Graining of Data via Inhomogeneous Diffusion Condensation. IEEE BigData (2019)
Michael Perlmutter, Feng Gao, Guy Wolf, Matthew J. Hirn
Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms. CoRR (2019)
Feng Gao, Guy Wolf, Matthew J. Hirn
Geometric Scattering for Graph Data Analysis. ICML (2019)
Michael Perlmutter, Feng Gao, Guy Wolf, Matthew J. Hirn
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds. CoRR (2019)
Nathan Brugnone, Alex Gonopolskiy, Mark W. Moyle, Manik Kuchroo, David van Dijk, Kevin R. Moon, Daniel Colón-Ramos, Guy Wolf, Matthew J. Hirn, Smita Krishnaswamy
Coarse Graining of Data via Inhomogeneous Diffusion Condensation. CoRR (2019)
Michael Perlmutter, Guy Wolf, Matthew J. Hirn
Geometric Scattering on Manifolds. CoRR (2018)
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
Kymatio: Scattering Transforms in Python. CoRR (2018)
Michael Eickenberg, Georgios Exarchakis, Matthew J. Hirn, Stéphane Mallat, Louis Thiry
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties. CoRR (2018)
Feng Gao, Guy Wolf, Matthew J. Hirn
Graph Classification with Geometric Scattering. CoRR (2018)
Xavier Brumwell, Paul Sinz, Kwang Jin Kim, Yue Qi, Matthew J. Hirn
Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction. CoRR (2018)
Matthew J. Hirn, Stéphane Mallat, Nicolas Poilvert
Wavelet Scattering Regression of Quantum Chemical Energies. Multiscale Model. Simul. 15 (2) (2017)
Michael Eickenberg, Georgios Exarchakis, Matthew J. Hirn, Stéphane Mallat
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities. NIPS (2017)
Nicholas F. Marshall, Matthew J. Hirn
Time Coupled Diffusion Maps. CoRR (2016)
Matthew J. Hirn, Nicolas Poilvert, Stéphane Mallat
Quantum Energy Regression using Scattering Transforms. CoRR (2015)
Matthew J. Hirn, David P. Widemann
Frames for subspaces of $\mathbb{C}^N$. CoRR (2014)
Ariel Herbert-Voss, Matthew J. Hirn, Frederick McCollum
Computing minimal interpolants in $C^{1, 1}(\mathbb{R}^d)$. CoRR (2014)
Martin Ehler, Matthew J. Hirn
Sparse endmember extraction and demixing. IGARSS (2012)
Ronald R. Coifman, Matthew J. Hirn
Diffusion maps for changing data CoRR (2012)
Ronald R. Coifman, Matthew J. Hirn
Bi-stochastic kernels via asymmetric affinity functions CoRR (2012)
John J. Benedetto, Wojciech Czaja, Justin Flake, Matthew J. Hirn
Frame based Kernel Methods for Automatic Classification in Hyperspectral Data. IGARSS (4) (2009)