9 citations · 10 across the 2 of their papers we have counts for
3 papers
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
Yosuke Oyama, Naoya Maruyama, Nikoli Dryden +6
We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model trai…
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism
Nikoli Dryden, Naoya Maruyama, Tom Benson +3
Scaling CNN training is necessary to keep up with growing datasets and reduce training time. We also see an emerging need to handle datasets with very large samples, where memory r…
Effective Quantization Approaches for Recurrent Neural Networks
Md Zahangir Alom, Adam T Moody, Naoya Maruyama +2
Deep learning, and in particular Recurrent Neural Networks (RNN) have shown superior accuracy in a large variety of tasks including machine translation, language understanding, and…