When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.
In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database.
Adversarial training (AT) is one of the most reliable methods for defending against adversarial attacks in machine learning.
Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models.
We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier.
Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining.