Suivre
Taro Sekiyama
Titre
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Année
On polymorphic gradual typing
Y Igarashi, T Sekiyama, A Igarashi
Proceedings of the ACM on Programming Languages 1 (ICFP), 1-29, 2017
69*2017
Weighted automata extraction from recurrent neural networks via regression on state spaces
T Okudono, M Waga, T Sekiyama, I Hasuo
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5306-5314, 2020
522020
Profile-guided memory optimization for deep neural networks
T Sekiyama, T Imamichi, H Imai, R Raymond
arXiv preprint arXiv:1804.10001, 2018
242018
Dynamic type inference for gradual Hindley–Milner typing
Y Miyazaki, T Sekiyama, A Igarashi
Proceedings of the ACM on Programming Languages 3 (POPL), 1-29, 2019
232019
Polymorphic manifest contracts, revised and resolved
T Sekiyama, A Igarashi, M Greenberg
ACM Transactions on Programming Languages and Systems 39 (1), 3:1--3:36, 2017
22*2017
Manifest contracts for datatypes
T Sekiyama, Y Nishida, A Igarashi
Proceedings of the 42th ACM SIGPLAN Symposium on Principles of Programming …, 2015
222015
Bidirectional learning for time-series models with hidden units
T Osogami, H Kajino, T Sekiyama
International Conference on Machine Learning, 2711-2720, 2017
212017
Automated Proof Synthesis for the Minimal Propositional Logic with Deep Neural Networks
T Sekiyama, K Suenaga
Asian Symposium on Programming Languages and Systems, 309-328, 2018
17*2018
Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images
M Sakamoto, H Nakano, K Zhao, T Sekiyama
arXiv preprint arXiv:1703.00311, 2017
172017
Weighted cascading convolutional neural networks
T Sekiyama, M Sakamoto, H Nakano, K Zhao
US Patent 10,599,978, 2020
152020
Real-time resource usage reduction in artificial neural networks
T Sekiyama, K Kawachiya, TD Le, Y Negishi
US Patent 10,268,951, 2019
142019
Large Model Support for Deep Learning in Caffe and Chainer
M Cho, T Le, U Finkler, H Imai, Y Negishi, T Sekiyama, S Vinod, V Zolotov, ...
SysML, 2018
132018
Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic
T Sekiyama, A Imanishi, K Suenaga
arXiv preprint arXiv:1706.06462, 2017
122017
Stateful manifest contracts
T Sekiyama, A Igarashi
Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming …, 2017
122017
Temporal Verification with Answer-Effect Modification: Dependent Temporal Type-and-Effect System with Delimited Continuations
T Sekiyama, H Unno
Proceedings of the ACM on Programming Languages 7 (POPL), 2079-2110, 2023
112023
Lung nodule classification by the combination of fusion classifier and cascaded convolutional neural networks
M Sakamoto, H Nakano, K Zhao, T Sekiyama
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018
102018
Involving CPUs into Multi-GPU Deep Learning
TD Le, T Sekiyama, Y Negishi, H Imai, K Kawachiya
Proceedings of the 2018 ACM/SPEC International Conference on Performance …, 2018
102018
Multi-GPU deep learning using CPUs
TD Le, H Imai, T Sekiyama, Y Negishi
US Patent 11,164,079, 2021
82021
Toward neural-network-guided program synthesis and verification
N Kobayashi, T Sekiyama, I Sato, H Unno
Static Analysis: 28th International Symposium, SAS 2021, Chicago, IL, USA …, 2021
82021
Handling polymorphic algebraic effects
T Sekiyama, A Igarashi
European Symposium on Programming, 353-380, 2019
82019
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