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6 papers · 2 filters
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps
Yifei Yang, Peng Wang, Xiaofan He +1
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often la…
A Sampling-Based Domain Generalization Study with Diffusion Generative Models
Ye Zhu, Yu Wu, Duo Xu +3
In this work, we investigate the domain generalization capabilities of diffusion models in the context of synthesizing images that are distinct from the training data. Instead of f…
Latent assimilation with implicit neural representations for unknown dynamics
Zhuoyuan Li, Bin Dong, Pingwen Zhang
Data assimilation is crucial in a wide range of applications, but it often faces challenges such as high computational costs due to data dimensionality and incomplete understanding…
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
Yizhen Yuan, Rui Kong, Shenghao Xie +2
Backdoor attack is a major threat to deep learning systems in safety-critical scenarios, which aims to trigger misbehavior of neural network models under attacker-controlled condit…
Multi-representations Space Separation based Graph-level Anomaly-aware Detection
Fu Lin, Haonan Gong, Mingkang Li +3
Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem.…
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
Yuling Jiao, Di Li, Xiliang Lu +2
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differentia…