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We present a novel theoretical framework for understanding the expressive power of normalizing flows.
Cubic-Spline Flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 1906
Earlier work this paper cites.
Principles of Mathematical Analysis
Rudin, W · 1976
Earlier work this paper cites.
On the theory of elliptically contoured distributions
Cambanis, S., Huang, S., and Simons, G · 1981
Earlier work this paper cites.
A characterization of spherical distributions
Eaton, M. L · 1986
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
Earlier work this paper cites.
The Normal Distribution , volume 100 of Lecture Notes in Statistics
Bryc, W · 1995
Earlier work this paper cites.
Gaussianization
Chen, S. and Gopinath, R · 2000
Earlier work this paper cites.
On Choosing and Bounding Probability Metrics
Gibbs, A. L. and Su, F. E · 2002
Earlier work this paper cites.
Dependence, Correlation and Gaussianity in Independent Component Analysis
Cardoso, J.-F · 2003
Earlier work this paper cites.
Matplotlib: A 2D graphics environment
Hunter, J. D · 2007
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
Earlier work this paper cites.
Data Structures for Statistical Computing in Python
McKinney, W · 2010
Earlier work this paper cites.
MCMC Using Hamiltonian Dynamics
Neal, R. M · 2011
Earlier work this paper cites.
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., and Bengio, Y · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Earlier work this paper cites.
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Adam: A Method for Stochastic Optimization, January 2017
Kingma, D. P. and Ba, J · 2017
Cited alongside, same era.
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Toth, P., Rezende, D. J., Jaegle, A., Racanière, S., Botev, A., and Higgins, I · 2020
Later among the works it cites.
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