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Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [article]

Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi
2021 arXiv   pre-print
Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.  ...  First, we propose Adversarial Mirrored Autoencoder (AMA), a variant of Adversarial Autoencoder, which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction  ...  Authors thank Ritesh Soni, Steven Loscalzo, Bayan Bruss, Samuel Sharpe and Jason Wittenbach for helpful discussions.  ... 
arXiv:2003.10713v3 fatcat:rhff735uxrborke4rwisjs6wm4

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges [article]

Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, Mohammad Sabokrou
2021 arXiv   pre-print
To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection  ...  Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently.  ...  Asano for the extremely useful discussions and for reviewing the paper prior to submission.  ... 
arXiv:2110.14051v1 fatcat:zqfomgebjjb3zl4snmkrojqdny

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Differentiable Shape Decoders DAY 1 -Jan 12, 2021 Collin, Anne-Sophie; De Vleeschouwer, Christophe 2174 Improved anomaly detection by training an autoencoder with skip connections on images corrupted  ...  DAY 3 -Jan 14, 2021 Azzam, Mohamed; Tohokantche Gnanha, Aurele; Wong, Hau-San; Wu, Si 762 Adversarially Constrained Interpolation for Unsupervised Domain Adaptation DAY 3 -Jan 14, 2021 Zhang  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Article numbers are based on specified topic areas and corresponding codes associated with the publication.  ...  ., +, TIM 2021 2504013 Memory Residual Regression Autoencoder for Bearing Fault Detection.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry

Deep Neural Networks [article]

Randall Balestriero, Richard Baraniuk
2017 arXiv   pre-print
Answering those points would provide theoretical perspectives for further developments based on a common ground.  ...  Some fundamental problems remain: (1) the lack of a mathematical framework providing an explicit and interpretable input-output formula for any topology, (2) quantification of DNNs stability regarding  ...  "ambiguous" inputs,anomaly detection, allow for semisup with state-of-the-art performances and unsupervised training, clustering Flat-Minima, Generalization: define the concept of generalization for DNNs  ... 
arXiv:1710.09302v3 fatcat:ohz2szqioze7rcovfcboefdxiy

Data science applications to string theory

Fabian Ruehle
2019 Physics reports  
These include various clustering and anomaly detection algorithms, support vector machines, and decision trees.  ...  We first introduce various algorithms and techniques for machine learning and data science.  ...  If one of the flux quanta is chosen for mutation, a random number drawn from a normal distribution with zero mean and variance σ = 0.01 is added to the flux.  ... 
doi:10.1016/j.physrep.2019.09.005 fatcat:i5jtac7qpfgyrmsmvsylyinaba

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction [article]

Leland McInnes, John Healy, James Melville
2020 arXiv   pre-print
The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance.  ...  UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.  ...  Acknowledgements e authors would like to thank Colin Weir, Rick Jardine, Brendan Fong, David Spivak and Dmitry Kobak for discussion and useful commentary on various dra s of this paper.  ... 
arXiv:1802.03426v3 fatcat:m47pbjy7vzcqbg56ncpq5aiyte

How to Cite this Document

V Lyubchich, N Oza, A Rhines, E Szekely, I Ebert-Uphoff, C Monteleoni
2017 Proceedings of the 7th International Workshop on Climate Informatics: CI 2017. NCAR Technical Note NCAR/TN-536+PROC   unpublished
For the 2017 workshop, participants convened at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado between September 20-22.  ...  With trendy names, such as "data science", "deep learning", and "big data analytics", coming and going, the climate informatics scientists do their best in unraveling the history of the Earth's climate  ...  Thanks to generous funding from the National Science Foundation, NCAR, and the Elsevier journal Artificial Intelligence, 14 early career authors were provided with travel grants to attend the workshop.  ... 
fatcat:uei2sgi4bnap7fn2qlmh57koni