Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Michiaki Tatsubori"'
Autor:
Naomi Simumba, Michiaki Tatsubori
Transfer learning is a technique wherein information learned by previously trained models is applied to new learning tasks. Typically, weights learned by a network pretrained on other datasets are copied or transferred to new networks. These new netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::468e8684687d5fbb00e0ceb6ce0acda6
https://doi.org/10.5194/egusphere-egu23-1502
https://doi.org/10.5194/egusphere-egu23-1502
While deep machine learning approaches are getting pervasively used in remote sensing and modeling the earth, difficulties due to the size of satellite data are always pains for scientists in implementing such experiential software. We present a prog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6f7c5a4576111316236b93eb23e1a6e
https://doi.org/10.5194/egusphere-egu23-3441
https://doi.org/10.5194/egusphere-egu23-3441
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Autor:
Michiaki Tatsubori, Takao Moriyama, Tatsuya Ishikawa, Paolo Fraccaro, Anne Jones, Blair Edwards, Julian Kuehnert, Sekou L. Remy
When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e.g. 5 minutes) is essential not to miss the cause of flooding in local regions. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::590682fa677196c8975232118c9221d7
http://arxiv.org/abs/2203.01277
http://arxiv.org/abs/2203.01277
Publikováno v:
LocalRec@SIGSPATIAL
Diverse points extraction is an important process in the fields of location-based services and automated driving, among others. While existing research has investigated the selection of semantically diverse locations, the selection of points of inter
Autor:
Kimura, D., Ono, M., Chaudhury, S., Kohita, R., Wachi, A., Agravante, D. J., Michiaki Tatsubori, Munawar, A., Gray, A.
Publikováno v:
Scopus-Elsevier
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76dbc317c2037b81132d36069005fbb4
http://arxiv.org/abs/2110.10963
http://arxiv.org/abs/2110.10963
Publikováno v:
SIGSPATIAL/GIS
Testing self-driving cars in different areas requires surrounding cars with accordingly different driving styles such as aggressive or conservative styles. A method of numerically measuring and differentiating human driving styles to create a virtual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed3f65d459fc99b5c95ec8ca1a5d50f2
http://arxiv.org/abs/2109.11720
http://arxiv.org/abs/2109.11720
Autor:
Ryosuke Kohita, Daiki Kimura, Asim Munawar, Michiaki Tatsubori, Akifumi Wachi, Subhajit Chaudhury
Publikováno v:
ACL/IJCNLP (Findings)
Scopus-Elsevier
Scopus-Elsevier
Autor:
Michiaki Tatsubori, Daiki Kimura, Asim Munawar, Ryuki Tachibana, Subhajit Chaudhury, Kartik Talamadupula
Publikováno v:
EMNLP (1)
Scopus-Elsevier
Scopus-Elsevier
We show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this issue, we propose Context Relevant Episodic State Truncation (CREST) for