Motion assistance apparatus enabled for neuro-rehabilitation of patients and for the promotion of exercise for the elderly. - Eiichirou Tanaka; Ryosuke Niwa; Kazuki Osawa; Keyaki Nakajima; Keiichi Muramatsu; Keiichi Watanuki; Shozo Saegusa; Louis Yuge (AIM, 2015)

Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks. - Kazuki Osawa; Rio Yokota (ICANN (2), 2017)

Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. - Kazuki Osawa; Akira Sekiya; Hiroki Naganuma; Rio Yokota (HPCS, 2017)

Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs. - Kazuki Osawa; Yohei Tsuji; Yuichiro Ueno; Akira Naruse; Rio Yokota; Satoshi Matsuoka (CoRR, 2018)

Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks. - Kazuki Osawa; Yohei Tsuji; Yuichiro Ueno; Akira Naruse; Rio Yokota; Satoshi Matsuoka (CVPR, 2019)

Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method. - Yohei Tsuji; Kazuki Osawa; Yuichiro Ueno; Akira Naruse; Rio Yokota; Satoshi Matsuoka (ICPP Workshops, 2019)

Practical Deep Learning with Bayesian Principles. - Kazuki Osawa; Siddharth Swaroop; Mohammad Emtiyaz Khan; Anirudh Jain; Runa Eschenhagen; Richard E. Turner; Rio Yokota (NeurIPS, 2019)

Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC. - Yuichiro Ueno; Kazuki Osawa; Yohei Tsuji; Akira Naruse; Rio Yokota (KDD, 2020)

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks. - Ryo Karakida; Kazuki Osawa (NeurIPS, 2020)

Development of the Image-based Flight and Tree Measurement System in a Forest using a Drone. - Keiji Kamei; Masahiro Kaneoka; Ken Yanai; Masaya Umemoto; Hiroki Yamaguchi; Kazuki Osawa (J. Robotics Netw. Artif. Life, 2020)

Neural Graph Databases. - Maciej Besta; Patrick Iff; Florian Scheidl; Kazuki Osawa; Nikoli Dryden; Michal Podstawski; Tiancheng Chen; Torsten Hoefler (LoG, 2022)

Efficient Quantized Sparse Matrix Operations on Tensor Cores. - Shigang Li 002; Kazuki Osawa; Torsten Hoefler (SC, 2022)

Scalable and Practical Natural Gradient for Large-Scale Deep Learning. - Kazuki Osawa; Yohei Tsuji; Yuichiro Ueno; Akira Naruse; Chuan-Sheng Foo; Rio Yokota (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. - Ryo Karakida; Tomoumi Takase; Tomohiro Hayase; Kazuki Osawa (ICML, 2023)

PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices. - Kazuki Osawa; Shigang Li 002; Torsten Hoefler (MLSys, 2023)

ASDL: A Unified Interface for Gradient Preconditioning in PyTorch. - Kazuki Osawa; Satoki Ishikawa; Rio Yokota; Shigang Li 002; Torsten Hoefler (CoRR, 2023)

Improving Continual Learning by Accurate Gradient Reconstructions of the Past. - Erik A. Daxberger; Siddharth Swaroop; Kazuki Osawa; Rio Yokota; Richard E. Turner; José Miguel Hernández-Lobato; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)