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Today, deep learning is increasingly applied in security-critical situations such as autonomous driving and medical diagnosis.
“Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality”, 2019
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Amirsina Torfi, Rouzbeh. Shirvani, Yaser Keneshloo, Nader Tavaf and Edward. Fox · 2003
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“Estimation of Non-Normalized Statistical Models by Score Matching”
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“A tutorial on energy-based learning”
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“Pattern Recognition and Machine Learning”, Information Science and Statistics
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Laurens van Maaten and Geoffrey Hinton · 2008
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Michalis Titsias · 2009
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Alex Krizhevsky · 2009
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“Noise-contrastive estimation: A new estimation principle for unnormalized statistical models”
Michael Gutmann and Aapo Hyvärinen · 2010
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“MNIST handwritten digit database”
Yann LeCun, Corinna Cortes and CJ Burges · 2010
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“Bayesian Learning via Stochastic Gradient Langevin Dynamics”
Max Welling and Yee Teh · 2011
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“Machine Learning, Etc: notMNIST Dataset”
Yaroslav Bulatov · 2011
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“Reading Digits in Natural Images with Unsupervised Feature Learning”
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu and Andrew. Ng · 2011
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Dennis Ulmer and Giovanni Cinà · 2012
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“Perfect Density Models cannot Guarantee Anomaly Detection”, 2021
Charline Lan and Laurent Dinh · 2012
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“A Review of Novelty Detection”
Marco.. Pimentel, David. Clifton, Lei Clifton and Lionel Tarassenko · 2014
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Danilo Rezende and Shakir Mohamed · 2015
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“Weight Uncertainty in Neural Network”
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu and Daan Wierstra · 2015
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“NICE: Non-linear Independent Components Estimation”, 2015
Laurent Dinh, David Krueger and Yoshua Bengio · 2015
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“Deep Learning Face Attributes in the Wild”
Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang · 2015
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“Explaining and Harnessing Adversarial Examples”, 2015
Ian. Goodfellow, Jonathon Shlens and Christian Szegedy · 2015
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“Uncertainty in Deep Learning”, 2016
Y. Gal · 2016
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“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”
Yarin Gal and Zoubin Ghahramani · 2016
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“Deep Structured Energy Based Models for Anomaly Detection”
Shuangfei Zhai, Yu Cheng, Weining Lu and Zhongfei Zhang · 2016
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“Deep Learning”
Ian Goodfellow, Yoshua Bengio and Aaron Courville · 2016
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Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser and Jianxiong Xiao · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Charles Fefferman, Sanjoy Mitter and Hariharan Narayanan · 2016
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“Deep Residual Learning for Image Recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Likelihood Ratios for Out-of-Distribution Detection”
Jie Ren, Peter. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon and Balaji Lakshminarayanan · 2019
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“WAIC, but Why? Generative Ensembles for Robust Anomaly Detection”, 2019
Hyunsun Choi, Eric Jang and Alexander. Alemi · 2019
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“Implicit Generation and Modeling with Energy Based Models”
Yilun Du and Igor Mordatch · 2019
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“Unbiased Implicit Variational Inference”
Michalis. Titsias and Francisco Ruiz · 2019
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“Uncertainty on Asynchronous Time Event Prediction”
Marin Biloš, Bertrand Charpentier and Stephan Günnemann · 2019
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“Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes”
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“Improved Variational Inference with Inverse Autoregressive Flow”
Durk Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever and Max Welling · 2016
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“Deep Learning in Medical Image Analysis”
Dinggang Shen, Guorong Wu and Heung-Il Suk · 2017
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“Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles”
Balaji Lakshminarayanan, Alexander Pritzel and Charles Blundell · 2017
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“A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks”
Dan Hendrycks and Kevin Gimpel · 2017
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“Density Estimation for Statistics and Data Analysis”
B.. Silverman · 2017
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Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio · 2017
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Matt Jordan, Justin Lewis and Alexandros Dimakis · 2019
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Dan Hendrycks and Thomas Dietterich · 2019
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Erik Nijkamp, Mitch Hill, Song-Chun Zhu and Ying Wu · 2019
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“A Survey of Deep Learning Techniques for Autonomous Driving”
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias and Gigel Macesanu · 2020
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“Why Normalizing Flows Fail to Detect Out-of-Distribution Data”
Polina Kirichenko, Pavel Izmailov and Andrew Wilson · 2020
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“Your classifier is secretly an energy based model and you should treat it like one”
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi and Kevin Swersky · 2020
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Bertrand Charpentier, Daniel Zügner and Stephan Günnemann · 2020
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“Uncertainty Estimation Using a Single Deep Deterministic Neural Network”
Joost Van, Lewis Smith, Yee Teh and Yarin Gal · 2020
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Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax and Balaji Lakshminarayanan · 2020
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Yen-Chang Hsu, Yilin Shen, Hongxia Jin and Zsolt Kira · 2020
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“Deep residual flow for out of distribution detection”
Ev Zisselman and Aviv Tamar · 2020
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“Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models”
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José. Núñez and Jordi Luque · 2020
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“Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features”
Robin Schirrmeister, Yuxuan Zhou, Tonio Ball and Dan Zhang · 2020
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“Sliced Score Matching: A Scalable Approach to Density and Score Estimation”
Yang Song, Sahaj Garg, Jiaxin Shi and Stefano Ermon · 2020
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“Energy-based Out-of-distribution Detection”
Weitang Liu, Xiaoyun Wang, John Owens and Yixuan Li · 2020
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“A Compact Convolutional Neural Network for Surface Defect Inspection”
Yibin Huang, C. Qiu, X. Wang, Shijun Wang and Kui Yuan · 2020
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“Flows for simultaneous manifold learning and density estimation”
Johann Brehmer and Kyle Cranmer · 2020
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“Understanding Deep Learning (Still) Requires Rethinking Generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2021
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“A Unifying Review of Deep and Shallow Anomaly Detection”
Lukas Ruff, Jacob. Kauffmann, Robert. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas. Dietterich and Klaus-Robert Müller · 2021
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Alexandre Verine, Benjamin Negrevergne, Fabrice Rossi and Yann Chevaleyre · 2021
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“On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty”, 2021
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key and Yarin Gal · 2021
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Warren Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi and Joshua Dillon · 2021
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Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky and David Duvenaud · 2021
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“How to Train Your Energy-Based Models”, 2021
Yang Song and Diederik. Kingma · 2021
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“Provably Robust Detection of Out-of-distribution Data (almost) for free”, 2021
Alexander Meinke, Julian Bitterwolf and Matthias Hein · 2021
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“No Conditional Models for Me: Training Joint EBMs on Mixed Continuous and Discrete Data”
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