Aura231
发表于 2025-4-1 05:07:05
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filicide
发表于 2025-4-1 09:32:58
Efficient Feature Interactions Learning with Gated Attention Transformerose a novel model named Gated Attention Transformer. In our method, .-order cross features are generated by crossing .-order cross features and .-order features, which uses the vanilla attention mechanism instead of the self-attention mechanism and is more explainable and efficient. In addition, as
Memorial
发表于 2025-4-1 13:30:31
Exploiting Intra and Inter-field Feature Interaction with Self-Attentive Network for CTR Predictionntion mechanism to aggregate all interactive embeddings. Finally, we assign DNNs in the prediction layer to generate the final output. Extensive experiments on three real public datasets show that IISAN achieves better performance than existing state-of-the-art approaches for CTR prediction.
tendinitis
发表于 2025-4-1 14:39:42
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Capitulate
发表于 2025-4-1 20:21:46
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nutrition
发表于 2025-4-2 01:25:32
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废墟
发表于 2025-4-2 03:58:14
Performance Evaluation of Pre-trained Models in Sarcasm Detection Tasktection task when computing resources are limited. However, XLNet may not be suitable for sarcasm detection task. In addition, we implement detailed grid search for four hyperparameters to investigate their impact on PTMs. The results show that learning rate is the most important hyperparameter. Fur
离开真充足
发表于 2025-4-2 09:21:40
AMBD: Attention Based Multi-Block Deep Learning Model for Warehouse Dwell Time Predictionepresent the loading task statuses of different trucks. On the basis of that, we propose a deep learning based multi-block dwell time prediction model, called .. It incorporates the loading ability of warehouse and the execution process of loading tasks of preceding trucks in the queue. Moreover, to
dapper
发表于 2025-4-2 14:58:23
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AMOR
发表于 2025-4-2 16:31:19
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