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Akira Naruse

2 accepted papers

2026

Sparser, Faster, Lighter Transformer Language Models

ICML 2026poster

Scaling autoregressive large language models (LLMs) has had an unprecedented impact, but at vast computational costs. In this work, we tackle these costs by leveraging unstructured sparsity within an LLM's feedforward layers, which account for the majority of its parameters and execution FLOPs. To a…

Cited by 0SourceScholar
2019

Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks

CVPR 2019poster

Large-scale distributed training of deep neural networks suffers from the generalization gap caused by the increase in the effective mini-batch size. Previous approaches try to solve this problem by varying the learning rate and batch size over epochs and layers, or some ad hoc modification of the b…

Cited by 124PDFcodeScholar