19 citations · 24 across the 5 of their papers we have counts for
3 papers · 1 filter
Sparser, Faster, Lighter Transformer Language Models
Edoardo Cetin, Stefano Peluchetti, Emilio Castillo +3
Scaling autoregressive large language models (LLMs) has driven unprecedented progress but comes with vast computational costs. In this work, we tackle these costs by leveraging uns…
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…
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…