| Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes T Akiba, S Suzuki, K Fukuda arXiv preprint arXiv:1711.04325, 2017 | 388 | 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 | 169 | 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 | 110 | 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 | 105 | 2014 |
| ChainerMN: Scalable distributed deep learning framework T Akiba, K Fukuda, S Suzuki arXiv preprint arXiv:1710.11351, 2017 | 90 | 2017 |
| Faster sequence homology searches by clustering subsequences S Suzuki, M Kakuta, T Ishida, Y Akiyama Bioinformatics 31 (8), 1183-1190, 2015 | 57 | 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 | 42 | 2019 |
| GHOSTM: a GPU-accelerated homology search tool for metagenomics S Suzuki, T Ishida, K Kurokawa, Y Akiyama PloS one 7 (5), e36060, 2012 | 34 | 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 | 26 | 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 | 18 | 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 | 15 | 2015 |
| 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 | 13 | 2017 |
| 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 | 11 | 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 | 10 | 2017 |
| Accelerating identification of frequent k-mers in DNA sequences with GPU S Suzuki, M Kakuta, T Ishida, Y Akiyama GTC, 2014 | 5 | 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 | 3 | 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 | 2 | 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 | | 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 |
