Weimplement our approach using TensorFlow[1]forefficient GPU-based computation and also use some components of TensorFlow Probability [4]2.

We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.

In real-world applications, event data is typically received in a streaming manner, where the distribution of patterns may shift over time. Additionally, privacy and memory constraints are commonly observed in practical scenarios, further compounding the challenges.

In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.

We establish Central Limit Theorems (CLT) for the derivatives of 2-layers NN models in(2) when n,p,d + in the proportional asymptotic regime(6). A weighted average of the gradients of the trained NN, up to an explicit additive correction, is proved to be asymptotically normal, where the variance of the limit can be estimatedexplicitly.

Our supplementary includes author statement, licensing, and implementation details of benchmark results for reproducibility. We bear all responsibilities for licensing, distributing, and maintaining our dataset. The proposed dataset is under the CC BY -NC-SA 4.0 license, while the code in the repository is For what purpose was the dataset created? The dataset comprises various types of annotations, including instances and topology relationships. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset?