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CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
Kiwan Maeng, Shivam Bharuka, Isabel Gao +8
The paper proposes and optimizes a partial recovery training system, CPR, for recommendation models. CPR relaxes the consistency requirement by enabling non-failed nodes to proceed…
A Closer Look at Codistillation for Distributed Training
Shagun Sodhani, Olivier Delalleau, Mahmoud Assran +3
Codistillation has been proposed as a mechanism to share knowledge among concurrently trained models by encouraging them to represent the same function through an auxiliary loss. T…
Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations
Mahmoud Assran, Nicolas Ballas, Lluis Castrejon +1
We investigate a strategy for improving the efficiency of contrastive learning of visual representations by leveraging a small amount of supervised information during pre-training.…
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
Mahmoud Assran, Michael Rabbat
We study Nesterov's accelerated gradient method with constant step-size and momentum parameters in the stochastic approximation setting (unbiased gradients with bounded variance) a…
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Florian Knoll, Tullie Murrell, Anuroop Sriram +8
Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space da…