Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge von Knoll, Florian, Murrell, Tullie, Sriram, Anuroop, Yakubova, Nafissa, Zbontar, Jure, Rabbat, Michael, Defazio, Aaron, Muckley, Matthew J., Sodickson, Daniel K., Zitnick, C. Lawrence, Recht, Michael P.

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A scaling calculus for the design and initialization of ReLU networks von Defazio, Aaron, Bottou, Léon

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End-to-End Variational Networks for Accelerated MRI Reconstruction von Sriram, Anuroop, Zbontar, Jure, Murrell, Tullie, Defazio, Aaron, Zitnick, C. Lawrence, Yakubova, Nafissa, Knoll, Florian, Johnson, Patricia

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Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study von Recht, Michael P., Zbontar, Jure, Sodickson, Daniel K., Knoll, Florian, Yakubova, Nafissa, Sriram, Anuroop, Murrell, Tullie, Defazio, Aaron, Rabbat, Michael, Rybak, Leon, Kline, Mitchell, Ciavarra, Gina, Alaia, Erin F., Samim, Mohammad, Walter, William R., Lin, Dana J., Lui, Yvonne W., Muckley, Matthew, Huang, Zhengnan, Johnson, Patricia, Stern, Ruben, Zitnick, C. Lawrence

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fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning von Knoll, Florian, Zbontar, Jure, Sriram, Anuroop, Muckley, Matthew J, Bruno, Mary, Defazio, Aaron, Parente, Marc, Geras, Krzysztof J, Katsnelson, Joe, Chandarana, Hersh, Zhang, Zizhao, Drozdzalv, Michal, Romero, Adriana, Rabbat, Michael, Vincent, Pascal, Pinkerton, James, Wang, Duo, Yakubova, Nafissa, Owens, Erich, Zitnick, C Lawrence, Recht, Michael P, Sodickson, Daniel K, Lui, Yvonne W

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Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization von Defazio, Aaron

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Prodigy: An Expeditiously Adaptive Parameter-Free Learner von Mishchenko, Konstantin, Defazio, Aaron

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Learning-Rate-Free Learning by D-Adaptation von Defazio, Aaron, Mishchenko, Konstantin

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Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry von Defazio, Aaron

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On the Curved Geometry of Accelerated Optimization von Defazio, Aaron

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Beyond Folklore: A Scaling Calculus for the Design and Initialization of ReLU Networks von Defazio, Aaron, Bottou, Léon

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Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization von Defazio, Aaron, Jelassi, Samy

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Dual Averaging is Surprisingly Effective for Deep Learning Optimization von Jelassi, Samy, Defazio, Aaron

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Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization von Defazio, Aaron

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Mechanic: A Learning Rate Tuner von Cutkosky, Ashok, Defazio, Aaron, Mehta, Harsh

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Prodigy: An Expeditiously Adaptive Parameter-Free Learner von Mishchenko, Konstantin, Defazio, Aaron

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Learning-Rate-Free Learning by D-Adaptation von Defazio, Aaron, Mishchenko, Konstantin

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Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method von Defazio, Aaron, Zhou, Baoyu, Xiao, Lin

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A Simple Practical Accelerated Method for Finite Sums von Defazio, Aaron

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Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry von Defazio, Aaron

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