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ORCIDKazuki Osawa
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2020 – today
- 2023
[j3]Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan:
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023)- [c12]Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa:
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. ICML 2023: 15809-15827 - [c11]Kazuki Osawa, Shigang Li, Torsten Hoefler:
PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices. MLSys 2023 - [i9]Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li, Torsten Hoefler:
ASDL: A Unified Interface for Gradient Preconditioning in PyTorch. CoRR abs/2305.04684 (2023) - 2022
- [j2]Kazuki Osawa
, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota:
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(1): 404-415 (2022) - [c10]Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler:
Neural Graph Databases. LoG 2022: 31 - [c9]Shigang Li, Kazuki Osawa, Torsten Hoefler:
Efficient Quantized Sparse Matrix Operations on Tensor Cores. SC 2022: 37:1-37:15 - [i8]Shigang Li, Kazuki Osawa, Torsten Hoefler:
Efficient Quantized Sparse Matrix Operations on Tensor Cores. CoRR abs/2209.06979 (2022) - [i7]Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler:
Neural Graph Databases. CoRR abs/2209.09732 (2022) - [i6]Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa:
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. CoRR abs/2210.02720 (2022) - [i5]Kazuki Osawa, Shigang Li, Torsten Hoefler:
PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices. CoRR abs/2211.14133 (2022) - 2020
- [j1]Keiji Kamei, Masahiro Kaneoka, Ken Yanai, Masaya Umemoto, Hiroki Yamaguchi, Kazuki Osawa:
Development of the Image-based Flight and Tree Measurement System in a Forest using a Drone. J. Robotics Netw. Artif. Life 7(2): 86-90 (2020) - [c8]Yuichiro Ueno, Kazuki Osawa, Yohei Tsuji, Akira Naruse, Rio Yokota:
Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC. KDD 2020: 2145-2153 - [c7]Ryo Karakida, Kazuki Osawa:
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks. NeurIPS 2020 - [i4]Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota:
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. CoRR abs/2002.06015 (2020) - [i3]Ryo Karakida, Kazuki Osawa:
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks. CoRR abs/2010.00879 (2020)
2010 – 2019
- 2019
- [c6]Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka:
Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks. CVPR 2019: 12359-12367 - [c5]Yohei Tsuji, Kazuki Osawa, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka:
Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method. ICPP Workshops 2019: 21:1-21:8 - [c4]Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota:
Practical Deep Learning with Bayesian Principles. NeurIPS 2019: 4289-4301 - [i2]Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan:
Practical Deep Learning with Bayesian Principles. CoRR abs/1906.02506 (2019) - 2018
- [i1]Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka:
Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs. CoRR abs/1811.12019 (2018) - 2017
- [c3]Kazuki Osawa, Rio Yokota:
Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks. ICANN (2) 2017: 459-466 - [c2]Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota:
Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. HPCS 2017: 186-192 - 2015
- [c1]Eiichirou Tanaka, Ryosuke Niwa, Kazuki Osawa, Keyaki Nakajima, Keiichi Muramatsu, Keiichi Watanuki, Shozo Saegusa, Louis Yuge:
Motion assistance apparatus enabled for neuro-rehabilitation of patients and for the promotion of exercise for the elderly. AIM 2015: 937-942
Coauthor Index
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