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An Empirical Study of Self-supervised Learning with Wasserstein Distance
We find that the model performance depends on the combination of TWD and probability model, and that the Jeffrey divergence regularization helps in model training.
Scalable and adaptive variational Bayes methods for Hawkes processes
Hawkes processes are often applied to model dependence and interaction phenomena in multivariate event data sets, such as neuronal spike trains, social interactions, and financial transactions.
Graph similarity learning for change-point detection in dynamic networks
The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to effectively compare the current graph and its recent history.
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
In this work we propose a model-agnostic algorithm that generates counterfactual ensemble explanations for time series anomaly detection models.
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