23 citations · 23 across the 1 of their papers we have counts for
3 papers
cs.DC2016
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs
Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas +2
We study the factors affecting training time in multi-device deep learning systems. Given a specification of a convolutional neural network, our goal is to minimize the time to tra…
stat.ML2016
Asynchrony begets Momentum, with an Application to Deep Learning
Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis +1
Asynchronous methods are widely used in deep learning, but have limited theoretical justification when applied to non-convex problems. We show that running stochastic gradient desc…
cs.LG2015★ 23 cited
Caffe con Troll: Shallow Ideas to Speed Up Deep Learning
Stefan Hadjis, Firas Abuzaid, Ce Zhang +1
We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristi…