Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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0302-9743 ne Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023..The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks i

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Diversifying Non-dissipative Reservoir Computing Dynamics,cs by discretizing an appropriately constrained ODE. In this paper, we propose alternative formulations of the reservoirs for EuSNs, aiming at improving the diversity of the resulting dynamics. Our empirical analysis points out the effectiveness of the proposed approaches on a large pool of time-series classification tasks.

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Elastizit?tsgesetz und Festigkeitshypothesenrs and slow inference speed make it challenging to deploy them on resource-constrained devices. Existing knowledge distillation methods use the point-to-point approach to transfer the knowledge, which restrains the ability to learn the higher-level semantic knowledge from the teacher network. In thi

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