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06/22/2021
Stochastic Polyak Stepsize with a Moving Target
We propose a new stochastic gradient method that uses recorded past loss...
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01/26/2021
Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization
We introduce MADGRAD, a novel optimization method in the family of AdaGr...
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10/20/2020
Dual Averaging is Surprisingly Effective for Deep Learning Optimization
First-order stochastic optimization methods are currently the most widel...
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10/01/2020
Understanding the Role of Momentum in Non-Convex Optimization: Practical Insights from a Lyapunov Analysis
Momentum methods are now used pervasively within the machine learning co...
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06/14/2020
On the convergence of the Stochastic Heavy Ball Method
We provide a comprehensive analysis of the Stochastic Heavy Ball (SHB) m...
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06/01/2020
Factorial Powers for Stochastic Optimization
The convergence rates for convex and non-convex optimization methods dep...
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04/14/2020
End-to-End Variational Networks for Accelerated MRI Reconstruction
The slow acquisition speed of magnetic resonance imaging (MRI) has led t...
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01/23/2020
MRI Banding Removal via Adversarial Training
MRI images reconstructed from sub-sampled data using deep learning techn...
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01/06/2020
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Purpose: To advance research in the field of machine learning for MR ima...
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12/02/2019
Offset Masking Improves Deep Learning based Accelerated MRI Reconstructions
Deep learning approaches to accelerated MRI take a matrix of sampled Fou...
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10/27/2019
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
Magnetic Resonance Image (MRI) acquisition is an inherently slow process...
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06/10/2019
Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks
In this work, we describe a set of rules for the design and initializati...
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02/02/2019
Methods of interpreting error estimates for grayscale image reconstructions
One representation of possible errors in a grayscale image reconstructio...
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12/11/2018
On the Curved Geometry of Accelerated Optimization
In this work we propose a differential geometric motivation for Nesterov...
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12/11/2018
Controlling Covariate Shift using Equilibrium Normalization of Weights
We introduce a new normalization technique that exhibits the fast conver...
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12/11/2018
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
The application of stochastic variance reduction to optimization has sho...
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11/21/2018
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...
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02/08/2016
A Simple Practical Accelerated Method for Finite Sums
We describe a novel optimization method for finite sums (such as empiric...
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10/09/2015
New Optimisation Methods for Machine Learning
A thesis submitted for the degree of Doctor of Philosophy of The Austral...
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04/16/2015
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
We apply stochastic average gradient (SAG) algorithms for training condi...
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10/31/2014
A Comparison of learning algorithms on the Arcade Learning Environment
Reinforcement learning agents have traditionally been evaluated on small...
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07/01/2014