Deep Gaussian Process autoencoders for novelty detection

[1]  M. Filippone,et al.  Deep Gaussian Process autoencoders for novelty detection , 2018, Machine Learning.

[2]  Kristian Kersting,et al.  Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks , 2018, AAAI.

[3]  Maurizio Filippone,et al.  A comparative evaluation of outlier detection algorithms: Experiments and analyses , 2018, Pattern Recognit..

[4]  Zoubin Ghahramani,et al.  Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks , 2017, 1707.02476.

[5]  Alex Kendall,et al.  Concrete Dropout , 2017, NIPS.

[6]  Léon Bottou,et al.  Wasserstein GAN , 2017, ArXiv.

[7]  Maurizio Filippone,et al.  Random Feature Expansions for Deep Gaussian Processes , 2016, ICML.

[8]  Samy Bengio,et al.  Density estimation using Real NVP , 2016, ICLR.

[9]  Hugo Larochelle,et al.  Neural Autoregressive Distribution Estimation , 2016, J. Mach. Learn. Res..

[10]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[11]  Daniel Hernández-Lobato,et al.  Deep Gaussian Processes for Regression using Approximate Expectation Propagation , 2016, ICML.

[12]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[13]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[14]  Neil D. Lawrence,et al.  Variationally Auto-Encoded Deep G aussian Processes , 2016, International Conference on Learning Representations.

[15]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[16]  Thomas G. Dietterich,et al.  A Meta-Analysis of the Anomaly Detection Problem , 2015 .

[17]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[18]  David A. Clifton,et al.  A review of novelty detection , 2014, Signal Process..

[19]  Ryan P. Adams,et al.  Avoiding pathologies in very deep networks , 2014, AISTATS.

[20]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Neil D. Lawrence,et al.  Deep Gaussian Processes , 2012, AISTATS.

[23]  Joachim Denzler,et al.  One-class classification with Gaussian processes , 2010, Pattern Recognit..

[24]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[25]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[26]  Hans-Peter Kriegel,et al.  A survey on unsupervised outlier detection in high‐dimensional numerical data , 2012, Stat. Anal. Data Min..

[27]  Alex Graves,et al.  Practical Variational Inference for Neural Networks , 2011, NIPS.

[28]  Thomas G. Dietterich,et al.  Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns , 2011, TOSN.

[29]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[30]  Lawrence K. Saul,et al.  Kernel Methods for Deep Learning , 2009, NIPS.

[31]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[32]  Zhi-Hua Zhou,et al.  Isolation Forest , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[33]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[34]  Clayton D. Scott,et al.  Robust kernel density estimation , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[35]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[36]  Aleksandar Lazarevic,et al.  Incremental Local Outlier Detection for Data Streams , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[37]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[38]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[39]  Julie Greensmith,et al.  Dendritic Cells for Anomaly Detection , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[40]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[41]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[42]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[43]  Wei Jiang,et al.  On-line outlier detection and data cleaning , 2004, Comput. Chem. Eng..

[44]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[45]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[46]  Keith Worden,et al.  DAMAGE DETECTION USING OUTLIER ANALYSIS , 2000 .

[47]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[48]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .