| Auteur | Rechercher : Wang, J.; Rechercher : Sun, S.1; Rechercher : Yu, Y. |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, December 8-14, 2019, Vancouver, Canada |
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| Résumé | Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches. In this work, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our framework unifies and extends many classical and recent novelty detection algorithms, and opens the way to exploit recent advances in flow-based neural density estimation. We adapt the multiple gradient descent algorithm to obtain the first efficient end-to-end implementation of our framework that is free of tuning hyperparameters. Extensive experiments over a number of real datasets confirm the efficacy of our proposed method against state-of-the-art alternatives. |
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| Date de publication | 2019-12-08 |
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| Maison d’édition | Neural Information Processing Systems Foundation |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 63b652a1-9f35-4c28-9b57-dde5b2b1fd29 |
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| Enregistrement créé | 2021-04-06 |
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| Enregistrement modifié | 2021-04-06 |
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