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Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2024-03-03
09:41		 Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)		 A preliminary study on deep causal discovery model for image classification
Ryohei Motoda, Megumi Nakao (Kyoto Univ.) MI2023-33		 Although saliency map used in image classification can visualize the regions correlated with predicted class, it cannot ... [more] MI2023-33
pp.11-14
ET 2015-10-31
15:10		 Oita Nippon Bunri Univ. (Yufuin Training Institute) Causal Map Generation with Pseudo-Haptic Feedback for Learning Historical Events
Takumi Horiguchi, Akihiro Kashihara (UEC) ET2015-50		 In learning history, it is important to understand causal relationships among historical events and a chain of the relat... [more] ET2015-50
pp.37-42
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