猛烈责骂
发表于 2025-3-30 09:28:37
Hua Yin,Shuo Huang,ZhiJian Wang,Yong Ye,WenHui Zhu
欢乐中国
发表于 2025-3-30 13:50:17
Yilin Chen,Tianxing Wu,Yunchang Liu,Yuxiang Wang,Guilin Qi
Hectic
发表于 2025-3-30 17:59:19
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MENT
发表于 2025-3-30 21:59:50
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过度
发表于 2025-3-31 02:28:30
Iterative Transfer Knowledge Distillation and Channel Pruning for Unsupervised Cross-Domain Compress, redundant channels in the student model are pruned to reduce the computational cost while retaining the model accuracy. In particular, the alternation of ACP and TKD ensures effective knowledge transfer, balancing the model size and its performance in the target domain. Experimental results demons
老人病学
发表于 2025-3-31 07:16:08
Iterative Transfer Knowledge Distillation and Channel Pruning for Unsupervised Cross-Domain Compress, redundant channels in the student model are pruned to reduce the computational cost while retaining the model accuracy. In particular, the alternation of ACP and TKD ensures effective knowledge transfer, balancing the model size and its performance in the target domain. Experimental results demons
豪华
发表于 2025-3-31 13:03:28
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Ruptured-Disk
发表于 2025-3-31 14:48:13
Aspect-Based Sentiment Classification Model Based on Multi-view Information Fusionom different perspectives has not been studied. To solve the above problems, an aspect-based sentiment classification model based on multi-view information fusion is proposed. By constructing an inference result set from the large language model (LLM), the LLM’s results are used to enhance the model
Amendment
发表于 2025-3-31 19:00:57
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BRACE
发表于 2025-3-31 23:49:14
GTGNN: Global Graph and Taxonomy Tree for Graph Neural Network Session-Based Recommendationnomy tree to learn user intent from the perspective of attention mechanism and historical distribution data respectively, simulating the decision-making process when interacting with new items. Meanwhile, to solve the problem that GNN cannot learn new items, zero-shot learning is introduced to infer