Zobrazeno 1 - 10
of 661
pro vyhledávání: '"Xu Bingbing"'
Autor:
Yuan, Yige, Xu, Bingbing, Tan, Hexiang, Sun, Fei, Xiao, Teng, Li, Wei, Shen, Huawei, Cheng, Xueqi
Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their outputs. However, current calibration methods for LLMs typically estima
Externí odkaz:
http://arxiv.org/abs/2411.13343
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at batch level, often exhibit suboptimal performance in complex real-world
Externí odkaz:
http://arxiv.org/abs/2410.09398
Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of-distribution (OOD) generalization has been relatively underexplored. In th
Externí odkaz:
http://arxiv.org/abs/2405.16224
As graph representation learning often suffers from label scarcity problems in real-world applications, researchers have proposed graph domain adaptation (GDA) as an effective knowledge-transfer paradigm across graphs. In particular, to enhance model
Externí odkaz:
http://arxiv.org/abs/2402.00904
Test-time adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution, offering the distinct advantage of not requiring access to training data and processes, especially valuable in the context of large
Externí odkaz:
http://arxiv.org/abs/2311.14402
Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for communities with high-importance vertices has recently received a great deal of att
Externí odkaz:
http://arxiv.org/abs/2308.13244
The data-hungry problem, characterized by insufficiency and low-quality of data, poses obstacles for deep learning models. Transfer learning has been a feasible way to transfer knowledge from high-quality external data of source domains to limited da
Externí odkaz:
http://arxiv.org/abs/2308.09499
The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current methods, mainly based on the data-driven paradigm such as data augment
Externí odkaz:
http://arxiv.org/abs/2305.15835
Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suffer from severe performance decline under unseen attacks, due to eithe
Externí odkaz:
http://arxiv.org/abs/2305.15792
Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomp
Externí odkaz:
http://arxiv.org/abs/2302.00873