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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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樓主: Hayes
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發(fā)表于 2025-3-23 10:31:14 | 只看該作者
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發(fā)表于 2025-3-23 16:55:17 | 只看該作者
https://doi.org/10.1007/978-3-662-38004-8e features can be effectively processed without incurring in unwanted information conflict or loss. By associating spatial and time-series information, our attention-based feature-alignment module enhances low-quality spatial regions around subject objects, thus, improving the performance of the mod
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發(fā)表于 2025-3-23 20:28:57 | 只看該作者
,Drehung bei kreisf?rmigem Querschnitt,istillation and replay, CPA learns representative information by memorizing character-representative prototypes and augmenting them in new learning phases to better distinguish different characters when the replay data is limited, and SGM augments the prototypes in a reliable way to improves the rel
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發(fā)表于 2025-3-24 01:42:12 | 只看該作者
Zug-, Druck- und Scherfestigkeit,ture distant texture correlations, contributing to the consistency and realism of the generated images. Experimental results on MNIST, CIFAR-10, CelebA-HQ, and ImageNet datasets show that our approach significantly improves the diversity and visual quality of the generated images.
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發(fā)表于 2025-3-24 03:48:22 | 只看該作者
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發(fā)表于 2025-3-24 18:14:50 | 只看該作者
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發(fā)表于 2025-3-24 21:45:27 | 只看該作者
,CSEDesc: CyberSecurity Event Detection with?Event Description, we employ an attention mechanism to fuse sentences with event information and obtain description-aware embeddings. Secondly, in the syntactic graph convolutional networks module, we use GCNs to encode the sentence, which exploits sentence structure information and improves the robustness of sentenc
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發(fā)表于 2025-3-25 02:26:15 | 只看該作者
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