[2U4-IS-2c] Machine learning

2023年6月7日(水) 13:30 〜 15:10 U会場 (遠隔)

Chair: Tohgoroh Matsui (Chubu university)

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13:30 〜 13:50

[2U4-IS-2c-01] Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning

We show how using staple techniques such as NN or GPR as building blocks of a more involved method can enhance ML capabilities in high dimension and or with sparse data

〇Sergei Manzhos¹, Shunsaku Tsuda¹, Hyojae Lee¹, Manabu Ihara¹ (1. Tokyo Institute of Technology)

[[Online, Regular]]

13:50 〜 14:10

[2U4-IS-2c-02] MANet: Mixed Attention Network for Visual Explanation

〇JINGJING BAI¹, Yoshinobu Kawahara^2,3 (1. Kyushu University, 2. Osaka Univeristy, 3. RIKEN)

[[Online, Regular]]

14:10 〜 14:30

[2U4-IS-2c-03] Emotion-guided Multi-modal Fusion for Personality Traits Recognition

〇Yusong WANG¹, Dongyuan LI¹, Jialun SHEN¹, Kotaro FUNAKOSHI¹, Manabu OKUMURA¹ (1. Tokyo Institute of Technology)

[[Online, Regular]]

14:30 〜 14:50

[2U4-IS-2c-04] Cognitive Workload Estimation from Raw EEG Signals using Deep Learning

〇Yoji Yamashita¹, Yukako Ito², Haluka Numata², Nagisa Masuda², Ikuko Yairi^1,2 (1. Sophia University, 2. Graduate School of Science and Technology, Sophia University)

[[Online, Regular]]

14:50 〜 15:10

[2U4-IS-2c-05] Rapid training of Perceiver in a low-cost computing environment

〇Kaiyu Suzuki¹, Tomofumi Matsuzawa¹ (1. Tokyo University of Science)

[[Online, Regular]]

セッション情報

[2U4-IS-2c] Machine learning

[2U4-IS-2c-01] Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning

We show how using staple techniques such as NN or GPR as building blocks of a more involved method can enhance ML capabilities in high dimension and or with sparse data

[2U4-IS-2c-02] MANet: Mixed Attention Network for Visual Explanation

[2U4-IS-2c-03] Emotion-guided Multi-modal Fusion for Personality Traits Recognition

[2U4-IS-2c-04] Cognitive Workload Estimation from Raw EEG Signals using Deep Learning

[2U4-IS-2c-05] Rapid training of Perceiver in a low-cost computing environment

認証