Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Shinichi Shirakawa"'
Autor:
Minami Masumoto, Ittetsu Fukuda, Suguru Furihata, Takahiro Arai, Tatsuto Kageyama, Kiyomi Ohmori, Shinichi Shirakawa, Junji Fukuda
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract Bhas 42 cell transformation assay (CTA) has been used to estimate the carcinogenic potential of chemicals by exposing Bhas 42 cells to carcinogenic stimuli to form colonies, referred to as transformed foci, on the confluent monolayer. Transf
Externí odkaz:
https://doaj.org/article/03613bb3b4194c44874fc2d68fc476e8
Publikováno v:
Neural Networks. 151:365-375
Conversational gestures have a crucial role in realizing natural interactions with virtual agents and robots. Data-driven approaches, such as deep learning and machine learning, are promising in constructing the gesture generation model, which automa
Publikováno v:
Applications of Evolutionary Computation ISBN: 9783031302282
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ff9ade5c8742343c2bbc280da52e6f6
https://doi.org/10.1007/978-3-031-30229-9_51
https://doi.org/10.1007/978-3-031-30229-9_51
Publikováno v:
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
Publikováno v:
2022 IEEE Symposium Series on Computational Intelligence (SSCI).
Motivated by the high prediction performance of convolutional neural networks (CNNs), several works have applied them to tabular datasets. As CNNs are built to accept images, several transformations of tabular data have been proposed to obtain images
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3819d39f598c4ee753315a150711c7b7
https://doi.org/10.21203/rs.3.rs-2174672/v1
https://doi.org/10.21203/rs.3.rs-2174672/v1
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion.
Autor:
Ryoki Hamano, Shinichi Shirakawa
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion.
Publikováno v:
IEEE Transactions on Evolutionary Computation. 24:1035-1049
We theoretically analyze the information geometric optimization (IGO), which is a unified framework of stochastic search algorithms for black-box optimization. The IGO framework has two parameters: 1) the learning rate and 2) the sample size, and the
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031159367
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40a67649158eb0c237f6e8f753cb81fb
https://doi.org/10.1007/978-3-031-15937-4_51
https://doi.org/10.1007/978-3-031-15937-4_51