Aaron Defazio
Aaron Defazio
Facebook AI Research
Verified email at anu.edu.au - Homepage
Title
Cited by
Cited by
Year
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
9562014
Finito: A faster, permutable incremental gradient method for big data problems
A Defazio, J Domke
International Conference on Machine Learning, 1125-1133, 2014
1462014
A simple practical accelerated method for finite sums
A Defazio
Advances in neural information processing systems, 676-684, 2016
772016
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
662015
fastMRI: An open dataset and benchmarks for accelerated MRI
J Zbontar, F Knoll, A Sriram, MJ Muckley, M Bruno, A Defazio, M Parente, ...
arXiv preprint arXiv:1811.08839, 2018
602018
A convex formulation for learning scale-free networks via submodular relaxation
A Defazio, TS Caetano
Advances in neural information processing systems, 1250-1258, 2012
342012
A comparison of learning algorithms on the arcade learning environment
A Defazio, T Graepel
arXiv preprint arXiv:1410.8620, 2014
222014
On the ineffectiveness of variance reduced optimization for deep learning
A Defazio, L Bottou
Advances in Neural Information Processing Systems, 1753-1763, 2019
132019
A graphical model formulation of collaborative filtering neighbourhood methods with fast maximum entropy training
A Defazio, T Caetano
arXiv preprint arXiv:1206.4622, 2012
112012
New optimisation methods for machine learning
A Defazio
arXiv preprint arXiv:1510.02533, 2015
92015
On the Curved Geometry of Accelerated Optimization
A Defazio
Advances in Neural Information Processing Systems, 1764-1773, 2019
32019
Natural language question answering over triple knowledge bases
A Defazio
Master's thesis, Australian National University, 2009
32009
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
A Sriram, J Zbontar, T Murrell, CL Zitnick, A Defazio, DK Sodickson
arXiv preprint arXiv:1910.12325, 2019
12019
Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks
A Defazio, L Bottou
arXiv preprint arXiv:1906.04267, 2019
12019
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
2020
MRI Banding Removal via Adversarial Training
A Defazio, T Murrell, MP Recht
arXiv preprint arXiv:2001.08699, 2020
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, ...
arXiv preprint arXiv:2001.02518, 2020
2020
Offset Masking Improves Deep Learning based Accelerated MRI Reconstructions
A Defazio
arXiv preprint arXiv:1912.01101, 2019
2019
Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry
A Defazio
arXiv, arXiv: 1912.01101, 2019
2019
Methods of interpreting error estimates for grayscale image reconstructions
A Defazio, M Tygert
arXiv preprint arXiv:1902.00608, 2019
2019
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