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

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https://doi.org/10.1007/978-3-031-44201-8artificial neural networks (NN); machine learning; deep learning; federated learning; convolutional neur

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Conference proceedings 2023g, ICANN?2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023..The 426 full papers and 9 short papers included in these proceedings were carefully reviewed and selected from 947 submissions.?ICANN is a dual-track conference, featuring tracks in brain inspired computing on

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,Properties of?the?Weighted and?Robust Implicitly Weighted Correlation Coefficients,text of template matching in image analysis. For a highly robust correlation coefficient inspired by the least weighted estimator, properties are derived and novel hypothesis tests are proposed. This robust measure is recommendable particularly for data contaminated by outliers (not only) in the context of image analysis.

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Linear-elastisches Werkstoffverhalten, performance can be achieved. The experiments on the Lhasa-Tibetan speech recognition task show that our proposed method is significantly superior to the baseline model, achieving a Tibetan word error rate of 4.12%, which is a 9.34% reduction compared to the baseline model and 1.06% lower compared to the existing pre-training model.

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Peter H?fele,Lothar Issler,Hans Ruo? way, Mutual Information Dropout can achieve effective improving generalization ability with evaluate neurons. Extensive experiments on Three datasets show that Mutual Information Dropout is much more efficient than many existing Dropout and can meanwhile achieve comparable or even better generalization ability.

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