Titlebook: Web Information Systems and Applications; 21st International C Cheqing Jin,Shiyu Yang,Yong Zhang Conference proceedings 2024 The Editor(s)

猛烈責(zé)罵

Hua Yin,Shuo Huang,ZhiJian Wang,Yong Ye,WenHui Zhu

歡樂中國

Yilin Chen,Tianxing Wu,Yunchang Liu,Yuxiang Wang,Guilin Qi

Hectic

MENT

過度

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

老人病學(xué)

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

豪華

Ruptured-Disk

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

BRACE

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