11 accepted papers
The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, necessitates maintaining rigorous logical integrity over extended horizons. However, prevalent memory preprocessing paradigms incur destructive de-contextuali…
Graph Neural Networks (GNNs) achieve strong performance in node classification tasks but exhibit substantial performance degradation under label noise. Despite recent advances in noise-robust learning, a principled approach that exploits the node-neighbor interdependencies inherent in graph data for…
In recent years, the proliferation of renewable energy sources has heightened the focus on networked energy systems. These systems face significant challenges due to the unpredictable nature of energy generation and consumption, as well as the complexity of managing numerous components and parameter…
Graph Neural Networks (GNNs) excel in many applications but struggle when trained with noisy labels, especially as noise can propagate through the graph structure. Despite recent progress in developing robust GNNs, few methods exploit the intrinsic properties of graph data to filter out noise. In th
Personalized Federated Learning (pFL) mitigates the impact of statistical heterogeneity on FL architecture to some extent by allowing participants to use personalized models based on local data distributions. The existing pFL methods optimize from the perspective of model structure, attempting to ad…
Transductive node prediction has been a popular learning setting in Graph Neural Networks (GNNs). It has been widely observed that the shortage of information flow between the distant nodes and intra-batch nodes (for large-scale graphs) often hurt the generalization of GNNs which overwhelmingly adop…
Graph Neural Networks (GNNs) have demonstrated superior performance in numerous real-world applications. Despite their success, recent studies have shown that GNNs are vulnerable under edge inference attacks aimed to infer the connectivity of a given pair of nodes. However, existing methods primaril…
Traffic flow prediction is of particular interest since its massive applications in intelligent transportation systems (ITS). The problem is challenging due to the complex spatio-temporal correlations and nonlinearities of traffic flows. However, existing methods based on the graph neural networks c…
Deep generative networks have achieved great success on distorted image restoration. However, existing deep learning approaches mainly focus on delicate module structure while ignoring the saturation problem. In this paper, we study the influence of different learning schemes on fitting capability a…
Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-wise robustness certificates for certain forms of bagging (e.g. partition aggregation). Beyond these particular forms, i…
Lung cancer has been the leading death cause in modern society. Early detection of pulmonary nodules can significantly improve the survival rate of lung cancer. In this paper, we propose a novel pulmonary nodule detection framework and a novel 3D dense attention module (DAM) which can efficiently ex…