Aaron Defazio
Aaron Defazio
Facebook AI Research
Zweryfikowany adres z anu.edu.au - Strona główna
Cytowane przez
Cytowane przez
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, 1646-1654, 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
Finito: A faster, permutable incremental gradient method for big data problems
A Defazio, J Domke
International Conference on Machine Learning, 1125-1133, 2014
A simple practical accelerated method for finite sums
A Defazio
Advances in neural information processing systems 29, 676-684, 2016
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
On the ineffectiveness of variance reduced optimization for deep learning
A Defazio, L Bottou
arXiv preprint arXiv:1812.04529, 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
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
A convex formulation for learning scale-free networks via submodular relaxation
AJ Defazio, TS Caetano
arXiv preprint arXiv:1407.2697, 2014
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
A comparison of learning algorithms on the arcade learning environment
A Defazio, T Graepel
arXiv preprint arXiv:1410.8620, 2014
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
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
On the curved geometry of accelerated optimization
A Defazio
Advances in Neural Information Processing Systems 32, 1766-1775, 2019
A graphical model formulation of collaborative filtering neighbourhood methods with fast maximum entropy training
A Defazio, T Caetano
arXiv preprint arXiv:1206.4622, 2012
New optimisation methods for machine learning
A Defazio
arXiv preprint arXiv:1510.02533, 2015
On the convergence of the stochastic heavy ball method
O Sebbouh, RM Gower, A Defazio
arXiv preprint arXiv:2006.07867, 2020
Understanding the role of momentum in non-convex optimization: Practical insights from a lyapunov analysis
A Defazio
arXiv preprint arXiv:2010.00406, 2020
MRI Banding Removal via Adversarial Training
A Defazio, T Murrell, MP Recht
arXiv preprint arXiv:2001.08699, 2020
Adaptivity without compromise: a momentumized, adaptive, dual averaged gradient method for stochastic optimization
A Defazio, S Jelassi
arXiv preprint arXiv:2101.11075, 2021
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