| Practical deep learning with Bayesian principles K Osawa, S Swaroop, MEE Khan, A Jain, R Eschenhagen, RE Turner, ... Advances in neural information processing systems 32, 2019 | 155 | 2019 |
| Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks K Osawa, Y Tsuji, Y Ueno, A Naruse, R Yokota, S Matsuoka The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp …, 2019 | 99* | 2019 |
| Scalable and practical natural gradient for large-scale deep learning K Osawa, Y Tsuji, Y Ueno, A Naruse, CS Foo, R Yokota IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 | 14 | 2020 |
| Accelerating matrix multiplication in deep learning by using low-rank approximation K Osawa, A Sekiya, H Naganuma, R Yokota 2017 International Conference on High Performance Computing & Simulation …, 2017 | 14 | 2017 |
| Understanding approximate fisher information for fast convergence of natural gradient descent in wide neural networks R Karakida, K Osawa Advances in neural information processing systems 33, 10891-10901, 2020 | 11 | 2020 |
| Rich information is affordable: A systematic performance analysis of second-order optimization using k-fac Y Ueno, K Osawa, Y Tsuji, A Naruse, R Yokota Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 6 | 2020 |
| Performance optimizations and analysis of distributed deep learning with approximated second-order optimization method Y Tsuji, K Osawa, Y Ueno, A Naruse, R Yokota, S Matsuoka Proceedings of the 48th International Conference on Parallel Processing …, 2019 | 6 | 2019 |
| Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs.(2018) K Osawa, Y Tsuji, Y Ueno, A Naruse, R Yokota, S Matsuoka arXiv preprint arXiv:1811.12019, 2018 | 4 | 2018 |
| Evaluating the compression efficiency of the filters in convolutional neural networks K Osawa, R Yokota International Conference on Artificial Neural Networks, 459-466, 2017 | 3 | 2017 |
| Understanding approximate Fisher information for fast convergence of natural gradient descent in wide neural networks R Karakida, K Osawa Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 124010, 2021 | | 2021 |
| Improvement of speed using low precision arithmetic in deep learning and performance evaluation of accelerator H Naganuma, A Sekiya, K Osawa, H Ootomo, Y Kuwamura, R Yokota IEICE Technical Report; IEICE Tech. Rep. 117 (238), 101-107, 2017 | | 2017 |
| Accelerating Convolutional Neural Networks Using Low-Rank Tensor Decomposition K Osawa, A Sekiya, H Naganuma, R Yokota IEICE Technical Report; IEICE Tech. Rep. 117 (238), 1-6, 2017 | | 2017 |
Rio YokotaAssociate Professor, Tokyo Institute of TechnologyBestätigte E-Mail-Adresse bei gsic.titech.ac.jp
