Следене
Shuji Suzuki
Shuji Suzuki
Preferred Networks, Inc.
Потвърден имейл адрес: preferred.jp
Заглавие
Позовавания
Позовавания
Година
Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes
T Akiba, S Suzuki, K Fukuda
arXiv preprint arXiv:1711.04325, 2017
3042017
Chainer: A deep learning framework for accelerating the research cycle
S Tokui, R Okuta, T Akiba, Y Niitani, T Ogawa, S Saito, S Suzuki, ...
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
1192019
MEGADOCK 4.0: an ultra–high-performance protein–protein docking software for heterogeneous supercomputers
M Ohue, T Shimoda, S Suzuki, Y Matsuzaki, T Ishida, Y Akiyama
Bioinformatics 30 (22), 3281-3283, 2014
892014
GHOSTX: an improved sequence homology search algorithm using a query suffix array and a database suffix array
S Suzuki, M Kakuta, T Ishida, Y Akiyama
PloS one 9 (8), e103833, 2014
822014
ChainerMN: Scalable distributed deep learning framework
T Akiba, K Fukuda, S Suzuki
arXiv preprint arXiv:1710.11351, 2017
792017
Faster sequence homology searches by clustering subsequences
S Suzuki, M Kakuta, T Ishida, Y Akiyama
Bioinformatics 31 (8), 1183-1190, 2015
522015
GHOSTM: a GPU-accelerated homology search tool for metagenomics
S Suzuki, T Ishida, K Kurokawa, Y Akiyama
PloS one 7 (5), e36060, 2012
332012
Sampling techniques for large-scale object detection from sparsely annotated objects
Y Niitani, T Akiba, T Kerola, T Ogawa, S Sano, S Suzuki
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
242019
Pfdet: 2nd place solution to open images challenge 2018 object detection track
T Akiba, T Kerola, Y Niitani, T Ogawa, S Sano, S Suzuki
arXiv preprint arXiv:1809.00778, 2018
212018
Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures
T Shimoda, S Suzuki, M Ohue, T Ishida, Y Akiyama
BMC systems biology 9 (1), 1-10, 2015
162015
GPU-acceleration of sequence homology searches with database subsequence clustering
S Suzuki, M Kakuta, T Ishida, Y Akiyama
PLoS one 11 (8), e0157338, 2016
152016
MEGADOCK-GPU: acceleration of protein-protein docking calculation on GPUs
T Shimoda, T Ishida, S Suzuki, M Ohue, Y Akiyama
Proceedings of the International Conference on Bioinformatics, Computational …, 2013
112013
A massively parallel sequence similarity search for metagenomic sequencing data
M Kakuta, S Suzuki, K Izawa, T Ishida, Y Akiyama
International journal of molecular sciences 18 (10), 2124, 2017
82017
GHOSTX: a fast sequence homology search tool for functional annotation of metagenomic data
S Suzuki, T Ishida, M Ohue, M Kakuta, Y Akiyama
Protein Function Prediction, 15-25, 2017
72017
Accelerating identification of frequent k-mers in DNA sequences with GPU
S Suzuki, M Kakuta, T Ishida, Y Akiyama
GTC, 2014
52014
Team PFDet's Methods for Open Images Challenge 2019
Y Niitani, T Ogawa, S Suzuki, T Akiba, T Kerola, K Ozaki, S Sano
arXiv preprint arXiv:1910.11534, 2019
22019
An ultra-fast computing pipeline for metagenome analysis with next-generation dna sequencers
S Suzuki, T Ishida, Y Akiyama
2012 SC Companion: High Performance Computing, Networking Storage and …, 2012
22012
Online-Codistillation Meets LARS, Going beyond the Limit of Data Parallelism in Deep Learning
S Murai, H Mikami, M Koyama, S Suzuki, T Akiba
2020 IEEE/ACM Fourth Workshop on Deep Learning on Supercomputers (DLS), 1-9, 2020
2020
Poster: An Ultra-Fast Computing Pipeline for Metagenome Analysis with Next-Generation DNA Sequencers
S Suzuki
2012 SC Companion: High Performance Computing, Networking Storage and …, 2012
2012
次世代シークエンサーから得られる大量メタゲノム情報の解析のための超高速パイプライン
T Ishida, S Suzuki, Y Akiyama
Tsubame ESJ.: e-science journal 6, 23-26, 2012
2012
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