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Found 5 papers, 1 papers with code
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period
Distributed parallel stochastic gradient descent algorithms are workhorses for large scale machine learning tasks.
Variance Reduced Local SGD with Lower Communication Complexity
To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in parallel to speed up training.
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent
Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are limited significantly in practice by the communication cost, making it difficult to achieve a linear time speedup.
Asynchronous Stochastic Composition Optimization with Variance Reduction
Composition optimization has drawn a lot of attention in a wide variety of machine learning domains from risk management to reinforcement learning.
Accelerating Stochastic Gradient Descent Using Antithetic Sampling
(Mini-batch) Stochastic Gradient Descent is a popular optimization method which has been applied to many machine learning applications.
