We can still ask the model which observation is the most likely in the next time step, or even several time steps ahead, and BP will produce the exact answer by analytically integrating over all possible past and future actions, and even over the unseen future observations when necessary. ...
In this section, we summarize the papers about the theoretic foundations and explanations of graph neural networks from various perspectives. Graph signal processing 7.1.1 From the spectral perspective of view, GCNs perform convolution operation on the input features in the spectral domain, which fol...
Over the past few years, several survey articles on anomaly detection methods [10], anomaly detection for fraud detection [[13], [14], [15]], and application of graph-based methods on anomaly detection [3,9,11,12] have been published. Our focus is to review papers on anomaly detection...
The learning is based on a convex optimization problem, called the sparse inverse covariance estimation, for which many efficient algorithms have been developed in the past few years. When dimensions are much larger than sample sizes, structure learning requires to consider statistical stability, in ...