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Conference

Anticipation, earliness, alarm cardinality: A new metric for industrial time-series anomaly detection

Subjects: fault detection time-series industry performance metrics ranking; fault detection; time-seriesFerrara; Italy

  • Source: SAFEPROCESS 2024, 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes ; SAFEPROCESS 2024 - 12th IFAC Symposium on Fault Detection, Supervision and Safety for

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Academic Journal

Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms

Subjects: Causal discovery; Time series; Noise-based

  • Source: ISSN: 2835-8856 ; Transactions on Machine Learning Research Journal ; https://hal.science/hal-04606168 ; Transactions on Machine Learning Research Journal, 2024 ;

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Conference

Koopman Ensembles for Probabilistic Time Series Forecasting

Subjects: Dynamical systems; Koopman operator; Uncertainty quantificationLyon; France

  • Source: EUSIPCO 2024 - 32nd European Signal Processing Conference ; https://hal.science/hal-04499908 ; EUSIPCO 2024 - 32nd European Signal Processing Conference, EURASIP, Aug 2024, Lyon, France

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Conference

Identifiability of total effects from abstractions of time series causal graphs

Subjects: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]SpainBarcelone, Spain

  • Source: 40th Conference on Uncertainty in Artificial Intelligencehttps://hal.science/hal-0425060240th Conference on Uncertainty in Artificial Intelligence, Association for Uncertainty in

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Academic Journal

Deep Species Distribution Modeling From Sentinel-2 Image Time-Series: A Global Scale Analysis on the Orchid Family

Subjects: Remote sensing; Macroecology; Data science

  • Source: ISSN: 1664-462X ; Frontiers in Plant Science ; https://hal.inrae.fr/hal-03693593 ; Frontiers in Plant Science, 2022, 13, pp.839327. ⟨10.3389/fpls.2022.839327⟩.

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Conference

Deep anomaly detection using self-supervised learning: application to time series of cellular data

Subjects: 1D-CNN; Self-supervised learning; Anomaly detectionPorto; PortugalPorto, Portugal

  • Source: ASPAI 2021 - 3rd International Conference on Advances in Signal Processing and Artificial Intelligence ; https://cea.hal.science/cea-03605065 ; ASPAI 2021 - 3rd International Conference on Advances in

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