most citedConsistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence

2 citations · 2 across the 2 of their papers we have counts for

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2 papers

cs.DC2021★ 2 cited

Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence

Karl Bäckström, Ivan Walulya, Marina Papatriantafilou +1

Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free al…

cs.LG2020

Controlled time series generation for automotive software-in-the-loop testing using GANs

Dhasarathy Parthasarathy, Karl Bäckström, Jens Henriksson +1

Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge. In cu…