Deep Gaussian Process autoencoders for novelty detection

Rémi Domingues; Pietro Michiardi; Jihane Zouaoui; Maurizio Filippone

doi:10.1007/s10994-018-5723-3

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Deep Gaussian Process autoencoders for novelty detection

Rémi Domingues Pietro Michiardi Jihane Zouaoui Maurizio Filippone

Year: 2018 Journal: Machine Learning Vol: 107 (8-10)Pages: 1363-1383 Publisher: Springer Science+Business Media

DOI: 10.1007/s10994-018-5723-3

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Keywords:

Novelty detection Autoencoder Novelty Artificial intelligence Computer science Inference Machine learning Scalability Gaussian process Deep learning Pattern recognition (psychology) Process (computing) Scale (ratio) Feature (linguistics) Gaussian

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Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Gaussian Processes and Bayesian Inference

Physical Sciences → Computer Science → Artificial Intelligence

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Deep Gaussian Process autoencoders for novelty detection

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