Michael Perlmutter
Michael Perlmutter
Department of Mathematics, University of California, Mathematics
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
Geometric wavelet scattering networks on compact Riemannian manifolds
M Perlmutter, F Gao, G Wolf, M Hirn
Mathematical and Scientific Machine Learning, 570-604, 2020
Understanding graph neural networks with asymmetric geometric scattering transforms
M Perlmutter, F Gao, G Wolf, M Hirn
arXiv preprint arXiv:1911.06253, 2019
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
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
Geometric scattering on manifolds
M Perlmutter, G Wolf, M Hirn
arXiv preprint arXiv:1812.06968, 2018
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
A Convergence Rate for Manifold Neural Networks
J Chew, D Needell, M Perlmutter
arXiv preprint arXiv:2212.12606, 2022
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
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 measure spaces
J Chew, M Hirn, S Krishnaswamy, D Needell, M Perlmutter, H Steach, ...
arXiv preprint arXiv:2208.08561, 2022
On a class of Calderón-Zygmund operators arising from projections of martingale transforms
M Perlmutter
Potential Analysis 42, 383-401, 2015
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
Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem?
Y Min, F Wenkel, M Perlmutter, G Wolf
arXiv preprint arXiv:2206.01506, 2022
A new approach to large deviations for the Ginzburg-Landau model
S Banerjee, A Budhiraja, M Perlmutter
Toward fast and provably accurate near-field ptychographic phase retrieval
M Iwen, M Perlmutter, MP Roach
Sampling Theory, Signal Processing, and Data Analysis 21 (1), 6, 2023
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, 2023
Learnable Filters for Geometric Scattering Modules
A Tong, F Wenkel, D Bhaskar, K Macdonald, J Grady, M Perlmutter, ...
arXiv preprint arXiv:2208.07458, 2022
Scattering statistics of generalized spatial poisson point processes
M Perlmutter, J He, M Hirn
ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022
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