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Aaron Defazio
Aaron Defazio
Fundamental AI Research team, Meta NY
Geverifieerd e-mailadres voor anu.edu.au - Homepage
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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
21102014
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
7922018
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
3192020
End-to-end variational networks for accelerated MRI reconstruction
A Sriram, J Zbontar, T Murrell, A Defazio, CL Zitnick, N Yakubova, F Knoll, ...
Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020
2752020
Finito: A faster, permutable incremental gradient method for big data problems
A Defazio, J Domke
International Conference on Machine Learning, 1125-1133, 2014
2072014
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
1962020
A simple practical accelerated method for finite sums
A Defazio
Advances in neural information processing systems 29, 2016
1642016
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
1292020
On the ineffectiveness of variance reduced optimization for deep learning
A Defazio, L Bottou
Advances in Neural Information Processing Systems 32, 2019
1052019
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
1042015
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
992020
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
912021
A momentumized, adaptive, dual averaged gradient method
A Defazio, S Jelassi
Journal of Machine Learning Research 23 (144), 1-34, 2022
622022
Learning-rate-free learning by d-adaptation
A Defazio, K Mishchenko
International Conference on Machine Learning, 7449-7479, 2023
472023
A convex formulation for learning scale-free networks via submodular relaxation
A Defazio, T Caetano
Advances in neural information processing systems 25, 2012
412012
A comparison of learning algorithms on the arcade learning environment
A Defazio, T Graepel
arXiv preprint arXiv:1410.8620, 2014
332014
On the curved geometry of accelerated optimization
A Defazio
Advances in Neural Information Processing Systems 32, 2019
262019
Stochastic polyak stepsize with a moving target
RM Gower, A Defazio, M Rabbat
arXiv preprint arXiv:2106.11851, 2021
202021
Understanding the role of momentum in non-convex optimization: Practical insights from a lyapunov analysis
A Defazio
arXiv preprint arXiv:2010.00406, 2020
192020
MRI banding removal via adversarial training
A Defazio, T Murrell, M Recht
Advances in Neural Information Processing Systems 33, 7660-7670, 2020
182020
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Artikelen 1–20