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

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Artificial Neural Networks and Machine Learning – ICANN 2024978-3-031-72332-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Thomas H. Ollendick,Michel Hersens of spherical data analysis. PGF kernels generalize RBF kernels in the context of spherical data. The properties of PGF kernels are studied. A semi-parametric learning algorithm is introduced to enable the use of PGF kernels with spherical data.

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Tailored Finite Point Operator Networks for Interface Problemsigh-contrast coefficients, resulting in intricate singularities that complicate resolution. The increasing adoption of deep learning techniques for solving partial differential equations has spurred our exploration of these methods for addressing interface problems. In this study, we introduce Tailo

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A Simple Task-Aware Contrastive Local Descriptor Selection Strategy for Few-Shot Learning Between Iner representational capabilities. These studies recognize the impact of background noise on classification performance. They typically filter query descriptors using all local descriptors in the support classes or engage in bidirectional selection between local descriptors in support and query sets.

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Adaptive Compression of the Latent Space in Variational Autoencodersver, one of the known challenges in using VAEs is the model’s sensitivity to its hyperparameters, such as the latent space size. This paper presents a simple extension of VAEs for automatically determining the optimal latent space size during the training process by gradually decreasing the latent s

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