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Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations.
Novelty detection for the identification of masses in mammograms
Tarassenko, L., Hayton, P., Cerneaz, N., and Brady, M · 1995
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LOF: Identifying density-based local outlier
Breunig, M. M., Kriegel, H. P., Ng, R. T., and Sander, J · 2000
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Support vector method for novelty detection
Schölkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J., and Platt, J. C · 2000
Earlier work this paper cites.
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Patcha, A. and Park, J.-M · 2007
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Isolation-based anomaly detection
Liu, F. T., Ting, K. M., and Zhou, Z. H · 2012
Cited alongside, same era.
Model-free robot anomaly detection
Hornung, R., Urbanek, H., Klodmann, J., Osendorfer, C., and Van Der Smagt, P · 2014
Cited alongside, same era.
A review of novelty detection
Pimentel, M. A., Clifton, D. A., Clifton, L., and Tarassenko, L · 2014
Cited alongside, same era.
Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
Cited alongside, same era.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Later among the works it cites.
Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Pyro: Deep Universal Probabilistic Programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D · 2018
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Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
Later among the works it cites.
FFJORD: Free-Form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2019
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