| Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks R Heckel, P Hand International Conference on Learning Representations, 2019 | 332 | 2019 |
| Phase retrieval under a generative prior P Hand, O Leong, V Voroninski Advances in Neural Information Processing Systems, 9136-9146, 2018 | 207 | 2018 |
| Global guarantees for enforcing deep generative priors by empirical risk P Hand, V Voroninski IEEE Transactions on Information Theory 66 (1), 401-418, 2019 | 169 | 2019 |
| Invertible generative models for inverse problems: mitigating representation error and dataset bias M Asim, M Daniels, O Leong, A Ahmed, P Hand International Conference on Machine Learning, 399-409, 2020 | 154 | 2020 |
| Stable optimizationless recovery from phaseless linear measurements L Demanet, P Hand Journal of Fourier Analysis and Applications 20 (1), 199-221, 2014 | 154 | 2014 |
| ROPTLIB: an object-oriented C++ library for optimization on Riemannian manifolds W Huang, PA Absil, KA Gallivan, P Hand ACM Transactions on Mathematical Software (TOMS) 44 (4), 1-21, 2018 | 74 | 2018 |
| An elementary proof of convex phase retrieval in the natural parameter space via the linear program PhaseMax P Hand, V Voroninski Communications in Mathematical Sciences 16 (7), 2047-2051, 2018 | 70 | 2018 |
| ShapeFit and ShapeKick for Robust, Scalable Structure from Motion T Goldstein, P Hand, C Lee, V Voroninski, S Soatto European Conference on Computer Vision, 289-304, 2016 | 56 | 2016 |
| Deriving Macroscopic Myocardial Conductivities by Homogenization of Microscopic Models P Hand, B Griffith, C Peskin Bulletin of Mathematical Biology 71, 1707-1726, 2009 | 51 | 2009 |
| Compressed Sensing from Phaseless Gaussian Measurements via Linear Programming in the Natural Parameter Space P Hand, V Voroninski arXiv preprint arXiv:1611.05985, 2016 | 47 | 2016 |
| Blind deconvolution by a steepest descent algorithm on a quotient manifold W Huang, P Hand SIAM Journal on Imaging Sciences 11 (4), 2757-2785, 2018 | 45 | 2018 |
| Scaling law for recovering the sparsest element in a subspace L Demanet, P Hand Information and Inference, 2014 | 44 | 2014 |
| A Provably Convergent Scheme for Compressive Sensing Under Random Generative Priors W Huang, P Hand, R Heckel, V Voroninski Journal of Fourier Analysis and Applications 27 (2), 1-34, 2021 | 38 | 2021 |
| Homogenization of an Electrophysiological Model for a Strand of Cardiac Myocytes with Gap Junctional and Electric-Field Coupling P Hand, C Peskin Bulletin of Mathematical Biology 72, 1408-1424, 2010 | 37 | 2010 |
| Global Guarantees for Blind Demodulation with Generative Priors P Hand, B Joshi Conference on Neural Information Processing Systems, 2019 | 32 | 2019 |
| Adaptive multiscale model for simulating cardiac conduction P Hand, B Griffith Proc. Natl. Acad. Sci USA 107, 14603-14608, 2010 | 31 | 2010 |
| PhaseLift is robust to a constant fraction of arbitrary errors P Hand arXiv, 2015 | 30 | 2015 |
| Corruption Robust Phase Retrieval via Linear Programming P Hand, V Voroninski arXiv preprint arXiv:1612.03547, 2016 | 29 | 2016 |
| Blind deconvolutional phase retrieval via convex programming A Ahmed, A Aghasi, P Hand Advances in Neural Information Processing Systems, 10030-10040, 2018 | 19 | 2018 |
| ShapeFit: Exact location recovery from corrupted pairwise directions P Hand, C Lee, V Voroninski arXiv preprint arXiv:1506.01437, 2015 | 19 | 2015 |
Vladislav VoroninskiCEO, Helm.aiE-mail confirmado em mit.edu
