| SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives A Defazio, F Bach, S Lacoste-Julien Advances in neural information processing systems 27, 2014 | 2491 | 2014 |
| fastMRI: An open dataset and benchmarks for accelerated MRI J Zbontar, F Knoll, A Sriram, T Murrell, Z Huang, MJ Muckley, A Defazio, ... arXiv preprint arXiv:1811.08839, 2018 | 1258 | 2018 |
| fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning F Knoll, J Zbontar, A Sriram, MJ Muckley, M Bruno, A Defazio, M Parente, ... Radiology: Artificial Intelligence 2 (1), e190007, 2020 | 567 | 2020 |
| End-to-end variational networks for accelerated MRI reconstruction A Sriram, J Zbontar, T Murrell, A Defazio, CL Zitnick, N Yakubova, F Knoll, ... International conference on medical image computing and computer-assisted …, 2020 | 487 | 2020 |
| Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge F Knoll, T Murrell, A Sriram, N Yakubova, J Zbontar, M Rabbat, A Defazio, ... Magnetic resonance in medicine 84 (6), 3054-3070, 2020 | 284 | 2020 |
| Finito: A faster, permutable incremental gradient method for big data problems A Defazio, J Domke International Conference on Machine Learning, 1125-1133, 2014 | 213 | 2014 |
| Using deep learning to accelerate knee MRI at 3 T: results of an interchangeability study MP Recht, J Zbontar, DK Sodickson, F Knoll, N Yakubova, A Sriram, ... American Journal of Roentgenology 215 (6), 1421-1429, 2020 | 180 | 2020 |
| A simple practical accelerated method for finite sums A Defazio Advances in neural information processing systems 29, 2016 | 171 | 2016 |
| Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball O Sebbouh, RM Gower, A Defazio Conference on Learning Theory, 3935-3971, 2021 | 165 | 2021 |
| On the ineffectiveness of variance reduced optimization for deep learning A Defazio, L Bottou Advances in Neural Information Processing Systems 32, 2019 | 155 | 2019 |
| GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction A Sriram, J Zbontar, T Murrell, CL Zitnick, A Defazio, DK Sodickson Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 149 | 2020 |
| Learning-rate-free learning by d-adaptation A Defazio, K Mishchenko International Conference on Machine Learning, 7449-7479, 2023 | 133 | 2023 |
| The road less scheduled A Defazio, X Yang, H Mehta, K Mishchenko, A Khaled, A Cutkosky Advances in Neural Information Processing Systems 37, 9974-10007, 2024 | 131 | 2024 |
| Prodigy: An expeditiously adaptive parameter-free learner K Mishchenko, A Defazio arXiv preprint arXiv:2306.06101, 2023 | 113 | 2023 |
| Non-uniform stochastic average gradient method for training conditional random fields M Schmidt, R Babanezhad, M Ahmed, A Defazio, A Clifton, A Sarkar artificial intelligence and statistics, 819-828, 2015 | 101 | 2015 |
| A momentumized, adaptive, dual averaged gradient method A Defazio, S Jelassi Journal of Machine Learning Research 23 (144), 1-34, 2022 | 98 | 2022 |
| A convex formulation for learning scale-free networks via submodular relaxation A Defazio, T Caetano Advances in neural information processing systems 25, 2012 | 43 | 2012 |
| Optimal linear decay learning rate schedules and further refinements A Defazio, A Cutkosky, H Mehta, K Mishchenko arXiv preprint arXiv:2310.07831, 2023 | 41* | 2023 |
| On the curved geometry of accelerated optimization A Defazio Advances in Neural Information Processing Systems 32, 2019 | 35 | 2019 |
| A comparison of learning algorithms on the arcade learning environment A Defazio, T Graepel arXiv preprint arXiv:1410.8620, 2014 | 34 | 2014 |
