| Convolutional neural networks analyzed via convolutional sparse coding V Papyan, Y Romano, M Elad The Journal of Machine Learning Research 18 (1), 2887-2938, 2017 | 301 | 2017 |
| Prevalence of neural collapse during the terminal phase of deep learning training V Papyan, XY Han, DL Donoho Proceedings of the National Academy of Sciences 117 (40), 24652-24663, 2020 | 287 | 2020 |
| Multi-scale patch-based image restoration V Papyan, M Elad IEEE Transactions on image processing 25 (1), 249-261, 2015 | 273 | 2015 |
| Convolutional dictionary learning via local processing V Papyan, Y Romano, J Sulam, M Elad Proceedings of the IEEE International Conference on Computer Vision, 5296-5304, 2017 | 155 | 2017 |
| Multilayer convolutional sparse modeling: Pursuit and dictionary learning J Sulam, V Papyan, Y Romano, M Elad IEEE Transactions on Signal Processing 66 (15), 4090-4104, 2018 | 142 | 2018 |
| Neural proximal gradient descent for compressive imaging M Mardani, Q Sun, D Donoho, V Papyan, H Monajemi, S Vasanawala, ... Advances in Neural Information Processing Systems 31, 2018 | 140 | 2018 |
| Working locally thinking globally: Theoretical guarantees for convolutional sparse coding V Papyan, J Sulam, M Elad IEEE Transactions on Signal Processing 65 (21), 5687-5701, 2017 | 134* | 2017 |
| Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks V Papyan, Y Romano, J Sulam, M Elad IEEE Signal Processing Magazine 35 (4), 72-89, 2018 | 131 | 2018 |
| Neural collapse under mse loss: Proximity to and dynamics on the central path XY Han, V Papyan, DL Donoho arXiv preprint arXiv:2106.02073, 2021 | 84 | 2021 |
| The full spectrum of deepnet hessians at scale: Dynamics with sgd training and sample size V Papyan arXiv preprint arXiv:1811.07062, 2018 | 81 | 2018 |
| Recurrent generative adversarial networks for proximal learning and automated compressive image recovery M Mardani, H Monajemi, V Papyan, S Vasanawala, D Donoho, J Pauly arXiv preprint arXiv:1711.10046, 2017 | 63 | 2017 |
| Measurements of three-level hierarchical structure in the outliers in the spectrum of deepnet hessians V Papyan arXiv preprint arXiv:1901.08244, 2019 | 59 | 2019 |
| Traces of class/cross-class structure pervade deep learning spectra V Papyan The Journal of Machine Learning Research 21 (1), 10197-10260, 2020 | 48 | 2020 |
| Degrees of freedom analysis of unrolled neural networks M Mardani, Q Sun, V Papyan, S Vasanawala, J Pauly, D Donoho arXiv preprint arXiv:1906.03742, 2019 | 9 | 2019 |
| Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video DJ Pangal, G Kugener, Y Zhu, A Sinha, V Unadkat, DJ Cote, B Strickland, ... Scientific reports 12 (1), 8137, 2022 | 8 | 2022 |
| Utility of the simulated outcomes following carotid artery laceration video data set for machine learning applications G Kugener, DJ Pangal, T Cardinal, C Collet, E Lechtholz-Zey, S Lasky, ... JAMA network open 5 (3), e223177-e223177, 2022 | 8 | 2022 |
| LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? D Glukhov, I Shumailov, Y Gal, N Papernot, V Papyan arXiv preprint arXiv:2307.10719, 2023 | 6 | 2023 |
| AI and the Digitized Photoarchive: Promoting Access and Discoverability E Prokop, XY Han, V Papyan, DL Donoho, CR Johnson Jr Art Documentation: Journal of the Art Libraries Society of North America 40 …, 2021 | 5 | 2021 |
| Multimodal latent variable analysis V Papyan, R Talmon Signal Processing 142, 178-187, 2018 | 4 | 2018 |
| Expert Surgeons and Deep Learning Models Can Predict the Outcome of Surgical Hemorrhage from One Minute of Video DJ Pangal, G Kugener, Y Zhu, A Sinha, V Unadkat, DJ Cote, B Strickland, ... medRxiv, 2022.01. 22.22269640, 2022 | 2 | 2022 |
Michael EladProfessor of Computer Science, Technion, IsraelVerified email at cs.technion.ac.il
