显而易见
发表于 2025-3-28 15:47:23
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不规则的跳动
发表于 2025-3-28 22:37:42
Generating Adversarial Examples by Adversarial Networks for Semi-supervised Learning a classifier that tries to classify the original samples and the adversarial examples consistently. We evaluate our model on several datasets, and the experimental results show that our model outperforms the state-of-the-art methods for semi-supervised learning. The experiments also demonstrate tha
规章
发表于 2025-3-29 02:49:23
Dual Path Convolutional Neural Network for Student Performance Prediction not trivial to construct a good predictive model for some majors with limited student samples. To address the above issues, we develop a novel end-to-end deep learning method and propose Dual Path Convolutional Neural Network (DPCNN) for student performance prediction. Moreover, we introduce multi-
NIP
发表于 2025-3-29 03:35:45
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JEER
发表于 2025-3-29 08:47:42
Personalized Book Recommendation Based on a Deep Learning Model and Metadatahe book recommendation problem using a deep learning model and various metadata that can infer the content and the quality of books without utilizing the actual content. Metadata, which include Library Congress Subject Heading (LCSH), book description, user ratings and reviews, which are widely avai
obligation
发表于 2025-3-29 11:57:02
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丑恶
发表于 2025-3-29 16:35:40
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中国纪念碑
发表于 2025-3-29 20:39:52
Co-purchaser Recommendation Based on Network Embeddinguncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has a high recommendation performance.
Minutes
发表于 2025-3-30 02:00:28
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箴言
发表于 2025-3-30 05:37:11
Memory-Augmented Attention Network for Sequential Recommendationttention network which is stacked on the memory layer. Finally, the mixture of long-term and short-term preference is feeded into the prediction layer to make recommendations. Extensive experiments on four real datasets show that MEANS outperforms various state-of-the-art sequential recommendation m