Shuji Suzuki
Shuji Suzuki
Preferred Networks, Inc.
Потвърден имейл адрес: preferred.jp
Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes
T Akiba, S Suzuki, K Fukuda
arXiv preprint arXiv:1711.04325, 2017
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
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
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
ChainerMN: Scalable distributed deep learning framework
T Akiba, K Fukuda, S Suzuki
arXiv preprint arXiv:1710.11351, 2017
Faster sequence homology searches by clustering subsequences
S Suzuki, M Kakuta, T Ishida, Y Akiyama
Bioinformatics 31 (8), 1183-1190, 2015
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
GHOSTM: a GPU-accelerated homology search tool for metagenomics
S Suzuki, T Ishida, K Kurokawa, Y Akiyama
PloS one 7 (5), e36060, 2012
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
GPU-acceleration of sequence homology searches with database subsequence clustering
S Suzuki, M Kakuta, T Ishida, Y Akiyama
PLoS one 11 (8), e0157338, 2016
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
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
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
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
Accelerating identification of frequent k-mers in DNA sequences with GPU
S Suzuki, M Kakuta, T Ishida, Y Akiyama
GTC, 2014
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
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
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
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
T Ishida, S Suzuki, Y Akiyama
Tsubame ESJ.: e-science journal 6, 23-26, 2012
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