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Titlebook: Optimization and Learning; 6th International Co Bernabé Dorronsoro,Francisco Chicano,El-Ghazali Ta Conference proceedings 2023 The Editor(s

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31#
發(fā)表于 2025-3-26 22:49:04 | 只看該作者
An Application of?Machine Learning Tools to?Predict the?Number of?Solutions for?a?Minimum Cardinaliter of subsets selected from a specified collection of subsets of the given set. It is well documented in the literature that the MCSCP has numerous, varied, and important industrial applications. For some of these applications, it would be useful to know if there are alternative optimums and the qua
32#
發(fā)表于 2025-3-27 04:03:14 | 只看該作者
33#
發(fā)表于 2025-3-27 06:28:13 | 只看該作者
34#
發(fā)表于 2025-3-27 10:50:39 | 只看該作者
Multi-objective Optimization of?Adhesive Bonding Process in?Constrained and?Noisy Settingsize break strength while minimizing cost), constrained (the process should not result in any visual damage to the materials, and stress tests should not result in adhesive failures), and uncertain (measuring the same process parameters several times lead to different break strength). Real-life physi
35#
發(fā)表于 2025-3-27 14:01:13 | 只看該作者
Conference proceedings 2023ng May 3–5, 2023.?The 32 full papers included in this book were carefully reviewed and selected from 78 submissions. They were organized in topical sections as follows: advanced optimization; learning; learning methods to enhance optimization tools; optimization applied to learning methods; and real
36#
發(fā)表于 2025-3-27 19:22:11 | 只看該作者
37#
發(fā)表于 2025-3-27 22:19:11 | 只看該作者
38#
發(fā)表于 2025-3-28 02:34:43 | 只看該作者
39#
發(fā)表于 2025-3-28 08:33:58 | 只看該作者
Neural Network Information Leakage Through Hidden Learning to state of the art; . a second one that takes as input the output of the first, retrieving sensitive information to solve a second classification task with good accuracy. Our result might expose important issues from an information security point of view, as for the use of artificial neural networks in sensible applications.
40#
發(fā)表于 2025-3-28 14:01:25 | 只看該作者
Real-Time Elastic Partial Shape Matching Using a?Neural Network-Based Adjoint Methodd where the hyper-elastic problem is solved using the feed-forward neural network and the adjoint problem is obtained through the backpropagation of the network. Our process improves the computation speed by multiple orders of magnitude while providing acceptable registration errors.
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