| 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 | 147 | 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 | 36 | 2023 |
| 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 | 33 | 2022 |
| Geometric wavelet scattering networks on compact Riemannian manifolds M Perlmutter, F Gao, G Wolf, M Hirn Mathematical and Scientific Machine Learning, 570-604, 2020 | 30 | 2020 |
| 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 | 21 | 2022 |
| 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 | 17 | 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 | 17 | 2021 |
| Geometric scattering on measure spaces J Chew, M Hirn, S Krishnaswamy, D Needell, M Perlmutter, H Steach, ... Applied and Computational Harmonic Analysis 70, 101635, 2024 | 16 | 2024 |
| 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 | 16 | 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 | 14 | 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 | 12 | 2019 |
| Learnable filters for geometric scattering modules A Tong, F Wenkel, D Bhaskar, K Macdonald, J Grady, M Perlmutter, ... IEEE Transactions on Signal Processing, 2024 | 11 | 2024 |
| Geometric scattering on manifolds M Perlmutter, G Wolf, M Hirn arXiv preprint arXiv:1812.06968, 2018 | 10 | 2018 |
| 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 | 9 | 2022 |
| 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 | 8* | 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 | 7 | 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 | 6 | 2022 |
| 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 | 5 | 2023 |
| A new approach to large deviations for the Ginzburg-Landau model S Banerjee, A Budhiraja, M Perlmutter | 5 | 2020 |
| Inverting spectrogram measurements via aliased wigner distribution deconvolution and angular synchronization. Information and Inference: A Journal of the IMA (2020) M Perlmutter, S Merhi, A Viswanathan, M Iwen | 5 | |
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