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Normalizing flows are an established approach for modelling complex probability densities through invertible transformations from a base distribution.
Sur la division des corps matériels en parties
Steinhaus, H · 1957
Earlier work this paper cites.
Some methods for classification and analysis of multivariate observations
MacQueen, J. B · 1967
Earlier work this paper cites.
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J · 1987
Earlier work this paper cites.
Mean shift, mode seeking, and clustering
Cheng, Y · 1995
Earlier work this paper cites.
Birch: An efficient data clustering method for very large databases
Zhang, T., Ramakrishnan, R., and Livny, M · 1996
Earlier work this paper cites.
Normalized cuts and image segmentation
Shi, J. and Malik, J · 2000
Earlier work this paper cites.
Nested sampling for general Bayesian computation
Skilling, J · 2006
Earlier work this paper cites.
A Taste of Topology
Runde, V · 2007
Earlier work this paper cites.
Web-scale k-means clustering
Sculley, D · 2010
Earlier work this paper cites.
emcee: The MCMC Hammer
Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
MADE: Masked Autoencoder for Distribution Estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
Variational inference with normalizing flows
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Hierarchical Clustering , pp. 195–211
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Masked autoregressive flow for density estimation
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A review of clustering techniques and developments
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Neural autoregressive flows
Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A · 2018
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Targeted free energy estimation via learned mappings
Wirnsberger, P., Ballard, A. J., Papamakarios, G., Abercrombie, S., Racanière, S., Pritzel, A., Jimenez Rezende, D., and Blundell, C · 2020
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Stochastic normalizing flows
Wu, H., Köhler, J., and Noé, F · 2020
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Nested sampling with any prior you like
Alsing, J. and Handley, W · 2021
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Densely connected normalizing flows
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Stabilizing invertible neural networks using mixture models
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A survey on bias and fairness in machine learning
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Fast likelihood-free cosmology with neural density estimators and active learning
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Relaxing bijectivity constraints with continuously indexed normalising flows
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Training normalizing flows with the information bottleneck for competitive generative classification
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Semi-supervised learning with normalizing flows
Izmailov, P., Kirichenko, P., Finzi, M., and Wilson, A. G · 2020
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Bevins, H., Handley, W., Lemos, P., Sims, P., de Lera Acedo, E., and Fialkov, A · 2022
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Semi-discrete normalizing flows through differentiable tessellation
Chen, R. T., Amos, B., and Nickel, M · 2022
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HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
Friedman, R. and Hassan, S · 2022
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Resampling base distributions of normalizing flows
Stimper, V., Schölkopf, B., and Miguel Hernandez-Lobato, J · 2022
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Marginal post-processing of Bayesian inference products with normalizing flows and kernel density estimators
Bevins, H. T. J., Handley, W. J., Lemos, P., Sims, P. H., de Lera Acedo, E., Fialkov, A., and Alsing, J · 2023
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