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Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat

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樓主: broach
31#
發(fā)表于 2025-3-26 20:56:26 | 只看該作者
32#
發(fā)表于 2025-3-27 03:22:51 | 只看該作者
33#
發(fā)表于 2025-3-27 08:15:13 | 只看該作者
More Than One-Hot: Chinese Macro Discourse Relation Recognition on Joint Relation Embeddingy considered the loss of prediction and ground truth before back-propagation in the form of a one-hot vector, which cannot reflect the relation coherence. To remedy this deficiency, we propose a macro discourse relation recognition model based on the Joint Relation Embedding (JRE). This model contai
34#
發(fā)表于 2025-3-27 09:41:23 | 只看該作者
Abstracting Inter-instance Relations and?Inter-label Correlation Simultaneously for?Sparse Multi-lababel learning (GNN-SML) is proposed. More specifically, latent representation for sparse multi-label instance sets is constructed, both involving inter-instance and inter-label relations. The attacking problem is that instance features or label sets are too sparse to be extracted effectively hidden
35#
發(fā)表于 2025-3-27 14:08:34 | 只看該作者
36#
發(fā)表于 2025-3-27 21:39:36 | 只看該作者
Multi-scale Feature Fusion Network with?Positional Normalization for?Single Image Dehazing MSFFP-Net which does not rely on the physical atmosphere scattering model, and the backbone of the proposed network is a multi-scale network (MSNet). The MSNet uses a up-sampling and down-sampling block to connect different scales, so the information in the net can be exchanged efficiently. The bas
37#
發(fā)表于 2025-3-27 22:04:26 | 只看該作者
38#
發(fā)表于 2025-3-28 03:39:14 | 只看該作者
Dependency Learning Graph Neural Network for?Multivariate Forecastingltivariate forecasting. However, most existing models fail to learn the dependencies between different time series. Lately, studies have shown that implementations of Graph Neural Networks in the field of Natural Language, Computer Vision, and Time Series have achieved exceptional performance. In th
39#
發(fā)表于 2025-3-28 06:25:07 | 只看該作者
Research on?Flame Detection Based on?Anchor-Free Algorithm FCOStting of anchor hyper-parameters and is insensitive to the change of object shape. Therefore, the improved anchor-free algorithm FCOS is introduced. Firstly, the Center-ness branch is replaced by the IoU prediction branch to make the bounding box location more accurate; then the random copy-pasting
40#
發(fā)表于 2025-3-28 11:01:48 | 只看該作者
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