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Probabilistic Modeling for Novelty Detection with Applications to Fraud
  Identification

Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification

5 March 2019
Rémi Domingues
    AAML
ArXivPDFHTML

Papers citing "Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification"

4 / 4 papers shown
Title
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in
  Gaussian Process Hybrid Deep Networks
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
BDL
AAML
68
171
0
08 Jul 2017
Distributed and parallel time series feature extraction for industrial
  big data applications
Distributed and parallel time series feature extraction for industrial big data applications
Maximilian Christ
A. Kempa-Liehr
M. Feindt
AI4TS
58
272
0
25 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
Statistical exponential families: A digest with flash cards
Statistical exponential families: A digest with flash cards
Frank Nielsen
Vincent Garcia
90
183
0
25 Nov 2009
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