[D-20] 情報論的学習理論と機械学習 | General Conference of the Institute of Electronics, Information and Communication Engineers

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Session Details

一般セッションInformation and Systems : General Session (D)

[D-20]情報論的学習理論と機械学習

Wed. Mar 26, 2025 9:00 AM - 12:00 PM JST
Wed. Mar 26, 2025 12:00 AM - 3:00 AM UTC

Building No.1 2F 12E(Tokyo City University)

Chair:Hisaya Kobayashi, AKIHIRO SUZUKI

9:00 AM - 9:15 AM JST(12:00 AM - 12:15 AM UTC)

[D-20-07]Course Planning Support Method based on Association Analysis between Subjects

〇Keisuke Abe¹, Kai Cheng¹ (1. Kyushu Sangyo University)

9:15 AM - 9:30 AM JST(12:15 AM - 12:30 AM UTC)

[D-20-08]Evaluation of the microseismic motion at the KAGRA site using neural network

〇Masaya Ohashi¹, Takaaki Yokozawa², Shoichi Oshino², Tatsuki Washimi³, Hirotaka Takahashi¹ (1. TCU, 2. ICRR, 3. NAOJ)

9:30 AM - 9:45 AM JST(12:30 AM - 12:45 AM UTC)

[D-20-09]Development of an AI Application for Floc Assessment in Water Purification Using Image Analysis

〇Akihiro Suzuki¹, Takashi Kawakami¹, Hiroki Matsuzaki¹, Shun Matsukawa¹, Taku Matsumoto¹, Yuichi Nemoto² (1. Hokkaido University of Science, 2. Maezawa Industries)

9:45 AM - 10:00 AM JST(12:45 AM - 1:00 AM UTC)

[D-20-10]Cooperative method of CMOS annealing and graph neural network for large-scale combinatorial optimization problems

〇Kento Hasegawa¹, Pablo Loyola², Andres Hoyos-Idrobo², Kazuo Ono¹, Toyotaro Suzumura^2,3, Yu Hirate², Masanao Yamaoka¹ (1. Hitachi, Ltd., 2. Rakuten Institute of Technology, 3. The University of Tokyo)

10:00 AM - 10:15 AM JST(1:00 AM - 1:15 AM UTC)

[D-20-11]Prototype-Based Efficient Clustered Federated Learning Framework for Non-IID Data Scenario

〇Jing Luo¹, Tomoaki Ohtsuki¹ (1. Keio University)

10:15 AM - 10:30 AM JST(1:15 AM - 1:30 AM UTC)

Break time

10:30 AM - 10:45 AM JST(1:30 AM - 1:45 AM UTC)

[D-20-17]Partitioned Learned Count-Min Sketch

〇Kyosuke Nishishita¹, Atsuki Sato¹, Yusuke Matsui¹ (1. The University of Tokyo)

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General Conference of the Institute of Electronics, Information and Communication Engineers

Session Details

[D-20]情報論的学習理論と機械学習

[D-20-07]Course Planning Support Method based on Association Analysis between Subjects

[D-20-08]Evaluation of the microseismic motion at the KAGRA site using neural network

[D-20-09]Development of an AI Application for Floc Assessment in Water Purification Using Image Analysis

[D-20-10]Cooperative method of CMOS annealing and graph neural network for large-scale combinatorial optimization problems

[D-20-11]Prototype-Based Efficient Clustered Federated Learning Framework for Non-IID Data Scenario

Break time

[D-20-12]Proposal of scenario forecasting method for long-term demand forecasting

[D-20-13]Fault Detection and Classification in Photo-Voltaic Systems using Multiclass SVM Algorithm

[D-20-14]畳み込みニューラルネットワークを用いた睡眠段階分類における
時系列データ補間の影響評価

[D-20-15]CNN for Sleep Stage Classification Using Multichannel Polysomnography Data

[D-20-16]Parameter Estimation of Decaying Oscillation using Autoencoder: Towards Gravitational Wave Ringdown Analysis

[D-20-17]Partitioned Learned Count-Min Sketch

Session Details

[D-20]情報論的学習理論と機械学習

[D-20-07]Course Planning Support Method based on Association Analysis between Subjects

[D-20-08]Evaluation of the microseismic motion at the KAGRA site using neural network

[D-20-09]Development of an AI Application for Floc Assessment in Water Purification Using Image Analysis

[D-20-10]Cooperative method of CMOS annealing and graph neural network for large-scale combinatorial optimization problems

[D-20-11]Prototype-Based Efficient Clustered Federated Learning Framework for Non-IID Data Scenario

Break time

[D-20-12]Proposal of scenario forecasting method for long-term demand forecasting

[D-20-13]Fault Detection and Classification in Photo-Voltaic Systems using Multiclass SVM Algorithm

[D-20-14]畳み込みニューラルネットワークを用いた睡眠段階分類における時系列データ補間の影響評価

[D-20-15]CNN for Sleep Stage Classification Using Multichannel Polysomnography Data

[D-20-16]Parameter Estimation of Decaying Oscillation using Autoencoder: Towards Gravitational Wave Ringdown Analysis

[D-20-17]Partitioned Learned Count-Min Sketch

[D-20-14]畳み込みニューラルネットワークを用いた睡眠段階分類における
時系列データ補間の影響評価