22 citations · 34 across the 6 of their papers we have counts for
3 papers · 1 filter
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Aleksandr Beznosikov, Peter Richtárik, Michael Diskin +2
Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transp…
Secure Distributed Training at Scale
Eduard Gorbunov, Alexander Borzunov, Michael Diskin +1
Many areas of deep learning benefit from using increasingly larger neural networks trained on public data, as is the case for pre-trained models for NLP and computer vision. Traini…
Distributed Deep Learning in Open Collaborations
Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin +13
Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High…