43 citations · 108 across the 8 of their papers we have counts for
3 papers · 1 filter
Distributed Learning over Unreliable Networks
Chen Yu, Hanlin Tang, Cedric Renggli +5
Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guar…
The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson +3
Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of comm…
SparCML: High-Performance Sparse Communication for Machine Learning
Cedric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh +2
Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data paral…