Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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0302-9743 : generative methods; and topics in computer vision...Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intel978-3-031-72331-5978-3-031-72332-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Artificial Neural Networks and Machine Learning – ICANN 202433rd International C

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Specific Language and Learning Disorders the perceptual similarity of portraits by mapping them into the latent space of a FaceNet embedding. Additionally, we present a new technique that fuses the output of an ensemble, to deliberately generate specific aspects of the recreated image.

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Revealing Unintentional Information Leakage in?Low-Dimensional Facial Portrait Representations the perceptual similarity of portraits by mapping them into the latent space of a FaceNet embedding. Additionally, we present a new technique that fuses the output of an ensemble, to deliberately generate specific aspects of the recreated image.

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Conference proceedings 2024ne Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024...The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics:?..Part I - theory of neural networks and machin

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Sara R. Berzenski,Tuppett M. Yatesrior information, i.e.?a likelihood-based perspective of training neural networks. Attention is also paid to very recently proposed regularized versions of robust neural networks; as a?novelty, these are expressed by means of quasi-likelihood and their connection to Bayesian reasoning is discussed as well.

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