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On the Curved Geometry of Accelerated Optimization
Aaron Defazio
In this work we propose a differential geometric motivation for Nesterov's accelerated gradient method (AGM) for strongly-convex problems. By considering the optimization procedure…
Controlling Covariate Shift using Balanced Normalization of Weights
Aaron Defazio, Léon Bottou
We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs. Th…
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
Aaron Defazio, Léon Bottou
The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success. The applicability of these techniques to the hard no…
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Jure Zbontar, Florian Knoll, Anuroop Sriram +24
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applicati…
Compressed sensing with a jackknife and a bootstrap
Mark Tygert, Rachel Ward, Jure Zbontar
Compressed sensing proposes to reconstruct more degrees of freedom in a signal than the number of values actually measured. Compressed sensing therefore risks introducing errors --…