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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe

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樓主: monster
51#
發(fā)表于 2025-3-30 10:57:30 | 只看該作者
Two-Stream Convolutional Neural Network for Multimodal Matchingork is trained using an extreme multiclass classification loss by viewing each multimodal data as a class. Then a finetuning step is performed by a ranking constraint. Experimental results on Flickr30k datasets demonstrate the effectiveness of the proposed network for multimodal matching.
52#
發(fā)表于 2025-3-30 15:48:51 | 只看該作者
Kernel Graph Convolutional Neural Networkshborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets. Code and data are publicly available (.).
53#
發(fā)表于 2025-3-30 18:10:50 | 只看該作者
54#
發(fā)表于 2025-3-31 00:08:37 | 只看該作者
55#
發(fā)表于 2025-3-31 01:59:12 | 只看該作者
56#
發(fā)表于 2025-3-31 05:10:18 | 只看該作者
A Multi-level Attention Model for Text Matchingard deviation (RRSD) will calculate the matching coverage score for all query words. Experiments on both question-answer task and learning to rank task have achieved state-of-the-art results compared to traditional statistical methods and deep neural network methods.
57#
發(fā)表于 2025-3-31 10:47:38 | 只看該作者
58#
發(fā)表于 2025-3-31 14:45:47 | 只看該作者
,Fernsehübertragungen auf Leitungen,coder, the representation of a word is augmented with its context. In the subtweet-level encoder, the event-based features are extracted in term of microblogs. Experimental results show that our model outperforms several strong baselines and achieves the state-of-the-art performance.
59#
發(fā)表于 2025-3-31 20:57:50 | 只看該作者
60#
發(fā)表于 2025-4-1 00:12:53 | 只看該作者
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