2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2020★ 2 cited
Scalable and Practical Natural Gradient for Large-Scale Deep Learning
Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno +3
Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. Previ…
cs.LG2018
Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks
Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno +3
Large-scale distributed training of deep neural networks suffer from the generalization gap caused by the increase in the effective mini-batch size. Previous approaches try to solv…
cs.DC2018
Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?
Jens Domke, Kazuaki Matsumura, Mohamed Wahib +6
Among the (uncontended) common wisdom in High-Performance Computing (HPC) is the applications' need for large amount of double-precision support in hardware. Hardware manufacturers…