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11/10/2021
Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training
With deep learning models rapidly growing in size, systems-level solutio...
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06/06/2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations
Communication bottleneck has been identified as a significant issue in d...
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05/10/2019
Densifying Assumed-sparse Tensors: Improving Memory Efficiency and MPI Collective Performance during Tensor Accumulation for Parallelized Training of Neural Machine Translation
Neural machine translation - using neural networks to translate human la...
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03/19/2019
Differentially Private Consensus-Based Distributed Optimization
Data privacy is an important concern in learning, when datasets contain ...
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02/13/2019
Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise
Data privacy is an important concern in machine learning, and is fundame...
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03/14/2018
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Performance of distributed optimization and learning systems is bottlene...
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11/14/2017