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Publication List - Rio Yokota 2019 (25 / 183 entries)
Journal Paper
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Akihiro Ida,
Hiroshi Nakashima,
Tasuku Hiraishi,
Ichitaro Yamazaki,
Rio Yokota,
Takeshi Iwashita.
QR Factorization of Block Low-rank Matrices with Weak Admissibility Condition,
Journal of Information Processing,
Vol. 12,
No. 4,
Nov. 2019.
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Ichitaro Yamazaki,
Akihiro Ida,
Rio Yokota,
Jack Dongarra.
Distributed Memory Lattice H-matrix Factorization,
The International Journal of High Performance Computing Applications,
Aug. 2019.
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Mustafa AbdulJabbar,
Mohammed Al Farhan,
Noha Al-Harthi,
Rui Chen,
Rio Yokota,
Hakan Bagci,
David Keyes.
Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering,
SIAM Journal on Scientific Computing,
Vol. 4,
No. 3,
pp. C245--C268,
June 2019.
International Conference (Reviewed)
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Kazuki Osawa,
Siddarth Swaroop,
Anirudh Jain,
Runa Eschenhagen,
Richard E. Turner,
Rio Yokota,
Mohammad Emtiyaz Khan.
Practical Deep Learning with Bayesian Principles,
The 33rd Conference on Neural Information Processing Systems,
Dec. 2019.
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Hiroyuki Ootomo,
Rio Yokota.
TSQR on TensorCores,
The International Conference for High Performance Computing, Networking, Storage, and Analysis,
Nov. 2019.
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Qianxing Ma,
Rio Yokota.
Runtime System for GPU-based Hierarchical LU factorization,
The International Conference for High Performance Computing, Networking, Storage, and Analysis,
Nov. 2019.
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Hiroki Naganuma,
Rio Yokota.
On Empirical Analysis of Layer-wised Learning Rate Schedule,
ACML 2019 Workshop on Statistics & Machine Learning Researchers,
Nov. 2019.
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Satoshi Ohshima,
Ichitaro Yamazaki,
Akihiro Ida,
Rio Yokota.
Optimization of Numerous Small Dense-Matrix–Vector Multiplications in H-matrix Arithmetic on GPU,
Auto-Tuning for Multicore and GPU (ATMG) In conjunction with the IEEE MCSoC-19,
Oct. 2019.
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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,
International Conference on Parallel Processing: The 1st Workshop on Parallel and Distributed Machine Learning,
Proceedings of the 48th International Conference on Parallel Processing: Workshops,
No. 21,
Aug. 2019.
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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,
IEEE/CVF Conference on Computer Vision and Pattern Recognition,
June 2019.
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Yuichiro Ueno,
Rio Yokota.
Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs,
19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID),
May 2019.
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Hiroki Naganuma,
Rio Yokota.
A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training,
2nd High Performance Machine Learning Workshop CCGrid2019 (HPML2019),
May 2019.
Domestic Conference (Reviewed)
International Conference (Not reviewed / Unknown)
Domestic Conference (Not reviewed / Unknown)
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Keita Yashima,
石川康太,
佐藤育郎,
Akihiro Nomura,
Rio Yokota,
SATOSHI MATSUOKA.
早期終了タイミングを予測する:深層学習における確率勾配の分布の変化点検出,
第22回情報論的学習理論ワークショップ (IBIS 2019),
Nov. 2019.
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Peter Spalthoff,
Rio Yokota.
Flexible and Simplistic Hierarchical Matrix-Based Fast Direct Solver,
The 170th Workshop on High Performance Computing,
July 2019.
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H. Ootomo,
R. Yokota.
GPU Implementation of TSQR Using Tensor Cores,
The 170th Workshop on High Performance Computing,
July 2019.
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Hiroki Naganuma,
Rio Yokota.
Smoothing of the Objective Function for Large Scale Parallel Deep Learning,
The 81st National Convention of IPSJ,
Mar. 2019.
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Hiroyuki Ootomo,
Rio Yokota.
Batched QR Decomposition Using TensorCores,
The 81st National Convention of IPSJ,
Mar. 2019.
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Hikaru Nakata,
Kazuki Osawa,
Rio Yokota.
Variational Inference in Deep Learning Using Natural Gradient Descent,
The 81st National Convention of IPSJ,
Mar. 2019.
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Kazuki Osawa,
Rio Yokota,
Chuan-Sheng Foo,
Vijay Chandrasekhar.
Second Order Optimization for Large Scale Parallel Deep Learning Through Analysis of the Fisher Information Matrix,
The 81st National Convention of IPSJ,
Mar. 2019.
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Rio Yokota,
Kazuki Osawa,
Yohei Tsuji,
Yuichiro Ueno,
Akira Naruse.
Second Order Optimization for Large Scale Parallel Deep Learning,
IEICE General Conference,
Mar. 2019.
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Hiroki Naganuma,
Rio Yokota.
Improving the Generalization Gap in Large-batch Training Using Noise Injection,
IEICE General Conference,
Mar. 2019.
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