[2S5-IS-2c] Machine learning

Wed. Jun 15, 2022 3:20 PM - 5:00 PM Room S (Online S)

Chair: Jun Sakuma (University of Tsukuba)

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3:20 PM - 3:40 PM

[2S5-IS-2c-01] Is Explainability a Prerequisite for Creativity?

Examining perceptions of AI generated music.

〇Caitlin Duncan¹, Naomi Imasato¹, Takayuki Nagai¹ (1. Osaka University)

Working-in-progress

3:40 PM - 4:00 PM

[2S5-IS-2c-02] Symbolic piano music understanding from large-scale pre-training

〇YINGFENG FU¹, Yusuke Tanimura^2,1, Hidemoto Nakada^2,1 (1. University of Tsukuba, 2. AIST)

Working-in-progress

4:00 PM - 4:20 PM

[2S5-IS-2c-03] Constraints-based explanation by visual feature learning

〇Jingbo Yan^1,2, Seiji Yamada^2,1,3 (1. The Graduate University for Advanced Studies, 2. National Institute of Informatics, 3. Tokyo Institute of Technology)

Regular

4:20 PM - 4:40 PM

[2S5-IS-2c-04] Learning Lifted Operator Models with Logical Neural Networks

〇Don Joven Agravante¹, Michiaki Tatsubori¹ (1. IBM Research)

Regular

4:40 PM - 5:00 PM

[2S5-IS-2c-05] Overfitting Problem in the Approximate Bayesian Computation Method Based on Maxima Weighted Isolation Kernel

Overfitting Problem in the ABC Method Based on MaxW iKernel

〇Iurii S. Nagornov¹ (1. The National Institute of Advanced Industrial Science and Technology)

Regular

Session information

[2S5-IS-2c] Machine learning

[2S5-IS-2c-01] Is Explainability a Prerequisite for Creativity?

Examining perceptions of AI generated music.

[2S5-IS-2c-02] Symbolic piano music understanding from large-scale pre-training

[2S5-IS-2c-03] Constraints-based explanation by visual feature learning

[2S5-IS-2c-04] Learning Lifted Operator Models with Logical Neural Networks

[2S5-IS-2c-05] Overfitting Problem in the Approximate Bayesian Computation Method Based on Maxima Weighted Isolation Kernel

Overfitting Problem in the ABC Method Based on MaxW iKernel

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