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Titlebook: Machine Learning, Optimization, and Data Science; 9th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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樓主: Truman
51#
發(fā)表于 2025-3-30 12:14:11 | 只看該作者
Integrated Human-AI Forecasting for Preventive Maintenance Task Duration Estimationrom two fleet maintenance facilities in Canada, containing more than 13,000 anonymized historical ship work orders (WO) ranging from 2017 to 2022. We used supervised learning algorithms to forecast the preventive maintenance task duration on this data, with and without expert task duration estimates
52#
發(fā)表于 2025-3-30 14:22:47 | 只看該作者
A Proximal Algorithm for?Network Slimming CNNs is optional. Using Kurdyka-?ojasiewicz assumptions, we establish global convergence of proximal NS. Lastly, we validate the efficacy of the proposed algorithm on VGGNet, DenseNet and ResNet on CIFAR 10/100. Our experiments demonstrate that after one round of training, proximal NS yields a CNN
53#
發(fā)表于 2025-3-30 18:09:50 | 只看該作者
54#
發(fā)表于 2025-3-30 21:12:02 | 只看該作者
Alternating Mixed-Integer Programming and?Neural Network Training for?Approximating Stochastic Two-Sour approach with the example of computing operating points in power systems by showing that the alternating approach provides improved first-stage decisions and a tighter approximation between the expected objective and its neural network approximation.
55#
發(fā)表于 2025-3-31 01:12:04 | 只看該作者
56#
發(fā)表于 2025-3-31 05:49:41 | 只看該作者
57#
發(fā)表于 2025-3-31 12:38:24 | 只看該作者
58#
發(fā)表于 2025-3-31 14:41:58 | 只看該作者
A Hybrid Steady-State Genetic Algorithm for?the?Minimum Conflict Spanning Tree Problemle 12 instances of type 1 benchmark instances whose conflict solutions are not known show that the proposed hybrid approach hSSGA is able to find better solution quality in comparison to state-of-the-art approaches. Also, hSSGA discovers new values on 8 instances out of 12 instances of type 1.
59#
發(fā)表于 2025-3-31 21:16:56 | 只看該作者
Evaluation of Selected Autoencoders in the Context of End-User Experience Managementerature as well-suited for detecting anomalies applied in this paper to hardware telemetry: Autoencoder (AE), Variational Autoencoder (VAE), and Deep Autoencoding Gaussian Mixture Model (DAEGMM). The results show that all three models provide anomaly detection in hardware telemetry data, though with
60#
發(fā)表于 2025-4-1 01:06:12 | 只看該作者
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