Michael Perlmutter
Michael Perlmutter
Department of Mathematics, Boise State University
Verified email at - Homepage
Cited by
Cited by
Magnet: A neural network for directed graphs
X Zhang, Y He, N Brugnone, M Perlmutter, M Hirn
Advances in neural information processing systems 34, 27003-27015, 2021
Understanding graph neural networks with generalized geometric scattering transforms
M Perlmutter, A Tong, F Gao, G Wolf, M Hirn
SIAM Journal on Mathematics of Data Science 5 (4), 873-898, 2023
Geometric wavelet scattering networks on compact Riemannian manifolds
M Perlmutter, F Gao, G Wolf, M Hirn
Mathematical and Scientific Machine Learning, 570-604, 2020
Msgnn: A spectral graph neural network based on a novel magnetic signed laplacian
Y He, M Perlmutter, G Reinert, M Cucuringu
Learning on Graphs Conference, 40: 1-40: 39, 2022
Can hybrid geometric scattering networks help solve the maximum clique problem?
Y Min, F Wenkel, M Perlmutter, G Wolf
Advances in Neural Information Processing Systems 35, 22713-22724, 2022
Inverting spectrogram measurements via aliased Wigner distribution deconvolution and angular synchronization
M Perlmutter, S Merhi, A Viswanathan, M Iwen
Information and Inference: A Journal of the IMA 10 (4), 1491-1531, 2021
Taxonomy of benchmarks in graph representation learning
R Liu, S Cantürk, F Wenkel, S McGuire, X Wang, A Little, L O’Bray, ...
Learning on Graphs Conference, 6: 1-6: 25, 2022
The manifold scattering transform for high-dimensional point cloud data
J Chew, H Steach, S Viswanath, HT Wu, M Hirn, D Needell, MD Vesely, ...
Topological, Algebraic and Geometric Learning Workshops 2022, 67-78, 2022
Geometric scattering on measure spaces
J Chew, M Hirn, S Krishnaswamy, D Needell, M Perlmutter, H Steach, ...
arXiv preprint arXiv:2208.08561, 2022
Overcoming oversmoothness in graph convolutional networks via hybrid scattering networks
F Wenkel, Y Min, M Hirn, M Perlmutter, G Wolf
arXiv preprint arXiv:2201.08932, 2022
Lower Lipschitz bounds for phase retrieval from locally supported measurements
MA Iwen, S Merhi, M Perlmutter
Applied and Computational Harmonic Analysis 47 (2), 526-538, 2019
Molecular graph generation via geometric scattering
D Bhaskar, J Grady, E Castro, M Perlmutter, S Krishnaswamy
2022 IEEE 32nd International Workshop on Machine Learning for Signal …, 2022
Geometric scattering on manifolds
M Perlmutter, G Wolf, M Hirn
arXiv preprint arXiv:1812.06968, 2018
Learnable filters for geometric scattering modules
A Tong, F Wenkel, D Bhaskar, K Macdonald, J Grady, M Perlmutter, ...
IEEE Transactions on Signal Processing, 2024
Modewise operators, the tensor restricted isometry property, and low-rank tensor recovery
CA Haselby, MA Iwen, D Needell, M Perlmutter, E Rebrova
Applied and Computational Harmonic Analysis 66, 161-192, 2023
A convergence rate for manifold neural networks
JA Chew, D Needell, M Perlmutter
2023 International Conference on Sampling Theory and Applications (SampTA), 1-5, 2023
On audio enhancement via online non-negative matrix factorization
A Sack, W Jiang, M Perlmutter, P Salanevich, D Needell
2022 56th Annual Conference on Information Sciences and Systems (CISS), 287-291, 2022
A new approach to large deviations for the Ginzburg-Landau model
S Banerjee, A Budhiraja, M Perlmutter
Phase Retrieval for via the Provably Accurate and Noise Robust Numerical Inversion of Spectrogram Measurements
M Iwen, M Perlmutter, N Sissouno, A Viswanathan
Journal of Fourier Analysis and Applications 29 (1), 8, 2023
On a class of Calderón-Zygmund operators arising from projections of martingale transforms
M Perlmutter
Potential Analysis 42, 383-401, 2015
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