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Titlebook: Advances in Neural Networks – ISNN 2024; 18th International S Xinyi Le,Zhijun Zhang Conference proceedings 2024 The Editor(s) (if applicabl

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樓主: culinary
31#
發(fā)表于 2025-3-26 22:34:25 | 只看該作者
https://doi.org/10.1007/978-3-662-07624-8 the images cut by MoCa, thereby increasing the diversity of the objects and enhancing the generalization ability of the model. DiffMoCa demonstrates its capabilities in extensive experiments, wherein it surpasses MoCa by 2.2% in mAP on the KITTI dataset under moderate conditions.
32#
發(fā)表于 2025-3-27 02:14:06 | 只看該作者
33#
發(fā)表于 2025-3-27 07:05:40 | 只看該作者
A Novel Entropy-Based Regularization for?NeRF to?View Synthesis in?Few-Shot Scenariosput demonstrated by diverse experiments. Our method provides a simple yet practical, computationally efficient solution for few-shot NeRF, paving the way for NeRF in real-world applications where image data is typically sparse.
34#
發(fā)表于 2025-3-27 11:09:06 | 只看該作者
35#
發(fā)表于 2025-3-27 13:54:28 | 只看該作者
A Novel Neurodynamic Approach to?Bilevel Quadratic Programmingoblem into convex programming. Furthermore, we introduce a projection neural network designed for resolving the MPCC efficiently. This neural network is structured to guarantee convergence from any initial point to the optimal solution of the original problem. The efficacy of our methodology is validated through a numerical simulation.
36#
發(fā)表于 2025-3-27 20:20:01 | 只看該作者
37#
發(fā)表于 2025-3-28 00:25:49 | 只看該作者
0302-9743 ementation of Neural Networks; Control Systems, Robotics, and Autonomous Driving; Fault Diagnosis and Intelligent Industry & Bio-signal, Bioinformatics, and Biomedical Engineering..978-981-97-4398-8978-981-97-4399-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
38#
發(fā)表于 2025-3-28 05:18:05 | 只看該作者
39#
發(fā)表于 2025-3-28 09:50:03 | 只看該作者
40#
發(fā)表于 2025-3-28 11:06:56 | 只看該作者
Grundzüge der Volkswirtschaftslehreilized to approximate the real distribution further. The experimental results (Demo page: .) show that our proposed HiFi-WaveGAN obtains 4.23 in the mean opinion score (MOS) metric for the 48?kHz SVS task, significantly outperforming other neural vocoders.
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