| 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 | 49* | 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 | 16 | 2020 |
| Understanding graph neural networks with asymmetric geometric scattering transforms M Perlmutter, F Gao, G Wolf, M Hirn arXiv preprint arXiv:1911.06253, 2019 | 16 | 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 | 12 | 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 | 7 | 2019 |
| Geometric scattering on manifolds M Perlmutter, G Wolf, M Hirn arXiv preprint arXiv:1812.06968, 2018 | 7 | 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 | 5 | 2022 |
| A Convergence Rate for Manifold Neural Networks J Chew, D Needell, M Perlmutter arXiv preprint arXiv:2212.12606, 2022 | 3 | 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 | 3 | 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 | 3 | 2022 |
| Geometric scattering on measure spaces J Chew, M Hirn, S Krishnaswamy, D Needell, M Perlmutter, H Steach, ... arXiv preprint arXiv:2208.08561, 2022 | 3 | 2022 |
| On a class of Calderón-Zygmund operators arising from projections of martingale transforms M Perlmutter Potential Analysis 42, 383-401, 2015 | 3 | 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 | 2 | 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 | 2 | 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 | 2 | 2022 |
| A new approach to large deviations for the Ginzburg-Landau model S Banerjee, A Budhiraja, M Perlmutter | 2 | 2020 |
| 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 | 1 | 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 | 1 | 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 | 1 | 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 | 1 | 2022 |
Matthew HirnCitadel LLCVerified email at citadel.com
