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A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences

Neural Information Processing Systems

Such causal graphs delineate the relations among alarms and can significantly aid engineers in identifying and rectifying faults. However, existing methods either ignore the topological relationships among devices or suffer from relatively low scalability and efficiency, failing to deliver high-quality responses in a timely manner.


Noether Embedding: Efficient Learning of Temporal Regularities Chi Gao

Neural Information Processing Systems

Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.