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Committee Date Time Place Paper Title / Authors Abstract Paper #
SANE 2024-06-05
13:30		 Kanagawa Information Technology R & D Center, MITSUBISHI Electric Corp.
(Primary: On-site, Secondary: Online)		 PolSAR RFI detection using degree of polarization
Yu Hashimoto, Akira Hirose, Ryo Natsuaki (UTokyo) SANE2024-9		  [more] SANE2024-9
pp.43-48
ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] 2024-02-19
13:45		 Hokkaido Hokkaido Univ. CLIP-based Zero-shot In-Distribution Detection
Atsuyuki Miyai, Qing Yu (UTokyo), Go Irie (TUS), Kiyoharu Aizawa (UTokyo) ITS2023-53 IE2023-42		 Extracting in-distribution (ID) images from noisy images scraped from the Internet is an important preprocessing for con... [more] ITS2023-53 IE2023-42
pp.40-45
NS 2023-05-12
11:15		 Tokyo Shinjuku Campus, Kogakuin University + Online
(Primary: On-site, Secondary: Online)		 A Study on the Performance of KVS Supported by Application Switch with Lexical Analyzer
Satoshi Ito (Kogakuin Univ.), Akihiro Nakao (Univ. of Tokyo), Masato Oguchi (Ochanomizu Univ.), Saneyasu Yamaguchi (Kogakuin Univ.) NS2023-18		  [more] NS2023-18
pp.43-48
HWS, VLD [detail] 2021-03-03
10:25		 Online Online Evaluation on Approximate Multiplier for CNN Calculation
Yuechuan Zhang, Masahiro Fujita, Takashi Matsumoto (UTokyo) VLD2020-68 HWS2020-43		 Improving the accuracy of a convolutional neural network (CNN) typically requires larger hardware with more energy consu... [more] VLD2020-68 HWS2020-43
pp.7-12
TL 2016-12-16
15:15		 Tokyo Room 303/304/305, Building #8, Waseda University [Invited Talk] Toward "Expectation Intelligence" -- Inspired by Viewpoint of Japanese Expressions --
Hideyuki Nakashima (Tokyo Univ.) TL2016-38		 The development of Deep Learning methods enabled us to solve several unattacked problems. However, every machine learni... [more] TL2016-38
pp.9-13
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