| Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t. A Gladkova, A Drozd, S Matsuoka Proceedings of the NAACL Student Research Workshop, 8-15, 2016 | 264 | 2016 |
| Word embeddings, analogies, and machine learning: Beyond king-man+ woman= queen A Drozd, A Gladkova, S Matsuoka Proceedings of coling 2016, the 26th international conference on …, 2016 | 181 | 2016 |
| Intrinsic evaluations of word embeddings: What can we do better? A Gladkova, A Drozd Proceedings of the 1st Workshop on Evaluating Vector-Space Representations …, 2016 | 114 | 2016 |
| The (too many) problems of analogical reasoning with word vectors A Rogers, A Drozd, B Li Proceedings of the 6th Joint Conference on Lexical and Computational …, 2017 | 100 | 2017 |
| Scaling word2vec on big corpus B Li, A Drozd, Y Guo, T Liu, S Matsuoka, X Du Data science and engineering 4, 157-175, 2019 | 66 | 2019 |
| Generalization in NLI: Ways (not) to go beyond simple heuristics P Bhargava, A Drozd, A Rogers arXiv preprint arXiv:2110.01518, 2021 | 61 | 2021 |
| Investigating different syntactic context types and context representations for learning word embeddings B Li, T Liu, Z Zhao, B Tang, A Drozd, A Rogers, X Du Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 40 | 2017 |
| Matrix engines for high performance computing: A paragon of performance or grasping at straws? J Domke, E Vatai, A Drozd, P ChenT, Y Oyama, L Zhang, S Salaria, ... 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2021 | 33 | 2021 |
| Subword-level composition functions for learning word embeddings B Li, A Drozd, T Liu, X Du Proceedings of the second workshop on subword/character level models, 38-48, 2018 | 32 | 2018 |
| Why globally re-shuffle? Revisiting data shuffling in large scale deep learning TT Nguyen, F Trahay, J Domke, A Drozd, E Vatai, J Liao, M Wahib, ... 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2022 | 28 | 2022 |
| Subcharacter information in Japanese embeddings: When is it worth it? M Karpinska, B Li, A Rogers, A Drozd Proceedings of the Workshop on the Relevance of Linguistic Structure in …, 2018 | 24 | 2018 |
| MLPerf™ HPC: A holistic benchmark suite for scientific machine learning on HPC systems S Farrell, M Emani, J Balma, L Drescher, A Drozd, A Fink, G Fox, D Kanter, ... 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing …, 2021 | 22 | 2021 |
| Scaling distributed deep learning workloads beyond the memory capacity with KARMA M Wahib, H Zhang, TT Nguyen, A Drozd, J Domke, L Zhang, R Takano, ... SC20: International Conference for High Performance Computing, Networking …, 2020 | 20 | 2020 |
| Outliers dimensions that disrupt transformers are driven by frequency G Puccetti, A Rogers, A Drozd, F Dell'Orletta arXiv preprint arXiv:2205.11380, 2022 | 18 | 2022 |
| Migrating legacy Fortran to Python while retaining Fortran-level performance through transpilation and type hints M Bysiek, A Drozd, S Matsuoka 2016 6th Workshop on Python for High-Performance and Scientific Computing …, 2016 | 18 | 2016 |
| GPU-accelerated large-scale distributed sorting coping with device memory capacity H Shamoto, K Shirahata, A Drozd, H Sato, S Matsuoka IEEE Transactions on Big Data 2 (1), 57-69, 2016 | 14 | 2016 |
| Myths and legends in high-performance computing S Matsuoka, J Domke, M Wahib, A Drozd, T Hoefler The International Journal of High Performance Computing Applications 37 (3-4 …, 2023 | 10 | 2023 |
| Overview of TSUBAME3. 0, green cloud supercomputer for convergence of HPC, AI and big-data S Matsuoka, T Endo, A Nukada, S Miura, A Nomura, H Sato, H Jitsumoto, ... Tsubame ESJ.: e-science journal/Tsubame e-science journal 編集室 編 16, 20-27, 2017 | 10 | 2017 |
| Python, performance, and natural language processing A Drozd, A Gladkova, S Matsuoka Proceedings of the 5th Workshop on Python for High-Performance and …, 2015 | 9 | 2015 |
| Large-scale distributed sorting for GPU-based heterogeneous supercomputers H Shamoto, K Shirahata, A Drozd, H Sato, S Matsuoka 2014 IEEE International Conference on Big Data (Big Data), 510-518, 2014 | 9 | 2014 |
Satoshi MatsuokaRIKEN Center for Computational Science (R-CCS) / Tokyo Institute of TechnologyBestätigte E-Mail-Adresse bei acm.org
