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This paper proposes a method to predict anticipatory outliers in embedding space that will become part of emerging topics, using geometric features and retrospective supervision, with high predictive performance on French news corpora.
The tweet shares an extensive tutorial on outlier detection techniques and a comprehensive textbook on the topic, sourced from a conference.
This paper evaluates different multivariate outlier detection methods (Z-score, Mahalanobis distances, PCA, Isolation Forest, and Hotelling's T-squared) for identifying irregular operations in district heating system data, proposing an ensemble approach based on agreement among the best-performing methods.
This paper explores optimizing Transformer neural network inference on FPGAs for real-time anomaly detection in financial time series, demonstrating efficient implementation on a PYNQ-Z2 board.
Promotion for the book 'Outlier Detection in Python' from Manning Publications, covering various outlier detection techniques with an emphasis on explainability.