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Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data.
Likelihood ratios for out-of-distribution detection
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. A. DePristo, J. V. Dillon, and B. Lakshminarayanan · 1906
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PYTHIA 6.4 physics and manual
T. Sjöstrand, S. Mrenna, and P. Skands · 2006
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A brief introduction to PYTHIA 8.1
T. Sjöstrand, S. Mrenna, and P. Skands · 2008
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Identifying boosted objects with N-subjettiness
J. Thaler and K. Van Tilburg · 2011
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FastJet user manual. (for version 3.0.2)
M. Cacciari, G. P. Salam, and G. Soyez · 2012
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Maximizing boosted top identification by minimizing N-subjettiness
J. Thaler and K. van Tilburg · 2012
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DELPHES 3: a modular framework for fast simulation of a generic collider experiment
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi · 2014
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Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
D. Hendrycks and K. Gimpel · 2016
Cited alongside, same era.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
S. Liang, Y. Li, and R. Srikant · 2017
Cited alongside, same era.
Deep Anomaly Detection with Outlier Exposure
D. Hendrycks, M. Mazeika, and T. Dietterich · 2018
Cited alongside, same era.
Do deep generative models know what they don’t know?
E. T. Nalisnick, A. Matsukawa, Y. W. Teh, D. Görür, and B. Lakshminarayanan · 2018
Cited alongside, same era.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
V. Böhm and U. Seljak · 2020
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B. Dai and U. Seljak · 2020
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Searching for New Physics with Deep Autoencoders
M. Farina, Y. Nakai, and D. Shih · 2020
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Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge, Nov. 2019a
G. Kasieczka, B. Nachman, and D. Shih · 2020
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R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge, Apr. 2019b
G. Kasieczka, B. Nachman, and D. Shih · 2020
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Lhco2020: Outcome of the challenge, 2020
G. Kasieczka, B. Nachman, and D. Shih · 2020
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B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen · 2018
Cited alongside, same era.
Dark matter benchmark models for early lhc run-2 searches: Report of the atlas/cms dark matter forum
D. Abercrombie, N. Akchurin, E. Akilli, J. A. Maestre, B. Allen, B. A. Gonzalez, J. Andrea, A. Arbey, G. Azuelos, P. Azzi, and et al · 2019
Cited alongside, same era.
scikit-hep/pyjet: 1.6.0, Dec. 2019
N. Dawe, E. Rodrigues, H. Schreiner, S. Meehan, M. R., D. Kalinkin, and B. Ostdiek · 2019
Cited alongside, same era.
Normalizing Flows: An Introduction and Review of Current Methods
I. Kobyzev, S. J. D. Prince, and M. A. Brubaker · 2019
Cited alongside, same era.
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Anomaly detection with density estimation
B. Nachman and D. Shih · 2020
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