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Titlebook: Machine Learning, Optimization, and Data Science; 8th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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樓主: Clinical-Trial
51#
發(fā)表于 2025-3-30 09:11:48 | 只看該作者
52#
發(fā)表于 2025-3-30 13:58:58 | 只看該作者
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發(fā)表于 2025-3-31 00:00:53 | 只看該作者
,Loss Function with?Memory for?Trustworthiness Threshold Learning: Case of?Face and?Facial Expressiowith makeup and occlusion is used for computational experiments in the partition that ensures high out of the training data distribution conditions, where only non-makeup and non-occluded images are used for CNN model ensemble training, while the test set contains only makeup and occluded images.
55#
發(fā)表于 2025-3-31 03:35:28 | 只看該作者
,LS-PON: A Prediction-Based Local Search for?Neural Architecture Search,LS-PON (Local Search with a Predicted Order of Neighbors) that uses linear regression models to order the exploration of neighbors during the search. LS-PON, unlike other prediction-based NAS methods, requires neither pre-sampling nor tuning. Our experiments on popular NAS benchmarks show that LS-PO
56#
發(fā)表于 2025-3-31 05:40:13 | 只看該作者
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發(fā)表于 2025-3-31 13:04:15 | 只看該作者
58#
發(fā)表于 2025-3-31 15:13:10 | 只看該作者
Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial ns. It is shown that utilization of both methods leads to efficient and complex sensitivity analysis of engineering structures, and it could be recommended to use combination of both techniques in industrial applications.
59#
發(fā)表于 2025-3-31 19:38:18 | 只看該作者
60#
發(fā)表于 2025-3-31 22:36:20 | 只看該作者
MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models, Forests, k-Nearest Neighbours, and Generative Adversarial Networks. Two missing values designs were tested, namely the Missing Completly at Random (MCAR) and Missing at Random (MAR) designs, with missing value rates ranging from 10% to 30%.
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