… filter framework was proposed. Firstly, the deep sparse … Visual tracking via locality sensitive
histograms[C] //Proceedings of … Why does unsupervised pre-training help deep learning?[J]. …

… graphs, vulnerable to data sparsity problems, and difficult to scale. This paper uses graph
convolution on local … Ngcf(neural graph collaborative filtering)[43]:基于 图卷积的协同过滤算法,…

… 将 KG 和协同过滤(collaborative filtering,CF)做联合训 练,从结构化知识,文本知识和视觉知识中
… Convolutional neural networks on graphs with fast localized spectral filtering[C]//Advances in …

… At the subgraph level, diverse local structures are generated … full graph level, a contrastive
view of the entire graph is constructed by combining similar subgraphs with the original graph …

… pre-training strategies in action recognition tasks are introduced, and the influence of pre-training …
and segments dataset for recognition and temporal localization[C]//Proceedings of the …

… ,such as contrastive language-image pre-training(CLIP), chat … understanding of the SAM
framework through a detailed … be due to overfitting on local cognitions. Introducing holistic …

… Model-level methods propose to apply the meta-learning framework which naturally fits into
… are exploredto describe the higher-order and local structural information of HINs. Within …

… Resource Description Framework (RDF) is utilized for graph construction consisting of visual
… not stuck at the local minimum. To sum up, the Q-Learning algorithm is shown in the Fig. 2. …

… Combining local contrast information to guide the lateral … , but this model does not require
pre training of data, has biological … Schematic diagram of PR curve and F index for each …

… 在短语 定位方面, Mu 等[59]提出了解耦的介入式图网络 (graph learning framework for phrase
… BERT: pre-training of deep bidirectional transformers for language understanding[C] //…