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Found 6 papers, 2 papers with code
Random Ensemble Reinforcement Learning for Traffic Signal Control
Moreover, we introduce the Update-To-Data (UTD) ratio to control the number of data reuses to improve the problem of low data utilization.
AnomMAN: Detect Anomaly on Multi-view Attributed Networks
It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks.
Enhanced countering adversarial attacks via input denoising and feature restoring
Despite the fact that deep neural networks (DNNs) have achieved prominent performance in various applications, it is well known that DNNs are vulnerable to adversarial examples/samples (AEs) with imperceptible perturbations in clean/original samples.
Defeating Catastrophic Forgetting via Enhanced Orthogonal Weights Modification
what is the upper bound of the learningable tasks sequentially for a given CL method?
Interpretable and Efficient Heterogeneous Graph Convolutional Network
However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for a target object, which hinders both effectiveness and interpretability; (2) they often need to generate intermediate meta-path based dense graphs, which leads to high computational complexity.
DISCO: Influence Maximization Meets Network Embedding and Deep Learning
As the number of required samples have been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency.
