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Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective.

, , , , and . IEEE Signal Process. Mag., 39 (4): 119-129 (2022)

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Distributional Data Augmentation Methods for Low Resource Language., , and . CoRR, (2023)Learning Time Series Counterfactuals via Latent Space Representations., , , and . DS, volume 12986 of Lecture Notes in Computer Science, page 369-384. Springer, (2021)Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective., , , , and . IEEE Signal Process. Mag., 39 (4): 119-129 (2022)Exploiting complex medical data with interpretable deep learning for adverse drug event prediction., , and . Artif. Intell. Medicine, (2020)Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients., , and . AIME, volume 12721 of Lecture Notes in Computer Science, page 338-348. Springer, (2021)Example-Based Feature Tweaking Using Random Forests., , , and . IRI, page 53-60. IEEE, (2019)Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions., , , and . CBMS, page 235-240. IEEE, (2021)Castor: Competing shapelets for fast and accurate time series classification., and . CoRR, (2024)Corporate governance performance ratings with machine learning., , , , , , , and . Intell. Syst. Account. Finance Manag., 29 (1): 50-68 (2022)Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients., , , and . Artif. Intell. Medicine, (January 2023)