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

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Artificial Neural Networks and Machine Learning – ICANN 202332nd International C

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Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay

SPER

Elastizit?tsgesetz und Festigkeitshypothesenuide the student model to obtain structural knowledge by distilling the relational knowledge between samples from a mini-batch through distance loss. 2RDA achieves excellent results and surpasses the state-of-the-art model compression methods on the GLUE benchmark, demonstrating the effectiveness of

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https://doi.org/10.1007/978-3-658-34187-9tree (WDT). Moreover, a graph convolution network (GCN) then is employed to learn syntactic representations of the WDT. Furthermore, the sentence-level attention and gating selection module are applied to capture the intrinsic interactions between sentence-level and document-level features. We evalu

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https://doi.org/10.1007/978-3-662-40223-8cale receptor aims to merge multi-level feature representations and learn scale and location knowledge. Finally, extensive experiments show that GFFN achieves competitive performance compared to the other mainstream methods in detecting five primary attributes of lettuce growth traits.

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Festigkeitslehre für Wirtschaftsingenieureairness. The ExFS method generally outperforms the compared filter-based feature selection methods in terms of fairness and achieves comparable results to the compared wrapper-based feature selection methods. In addition, our method can provide explanations for the rationale underlying this fairness

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