Fetching the paper…
Reading the bibliography…
Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently.
Emerging convolutions for generative normalizing flows
Hoogeboom, E., Berg, R. v. d., and Welling, M. (2019) · 1901
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
Polyak, B. T. and Juditsky, A. B. (1992) · 1992
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P. (1999) · 1999
Earlier work this paper cites.
An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. (1999) · 1999
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001) · 2001
Earlier work this paper cites.
Monotone and partially monotone neural networks
Daniels, H. and Velikova, M. (2010) · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
Earlier work this paper cites.
Inductive principles for restricted boltzmann machine learning
Marlin, B., Swersky, K., Chen, B., and Freitas, N. (2010) · 2010
Earlier work this paper cites.
Density estimation by dual ascent of the log-likelihood
Tabak, E. G., Vanden-Eijnden, E., et al. (2010) · 2010
Earlier work this paper cites.
The neural autoregressive distribution estimator
Larochelle, H. and Murray, I. (2011) · 2011
Earlier work this paper cites.
Lecture 6.5-rmsprop, coursera: Neural networks for machine learning
Tieleman, T. and Hinton, G. (2012) · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
Cited alongside, same era.
High-dimensional probability estimation with deep density models
Rippel, O. and Adams, R. P. (2013) · 2013
Cited alongside, same era.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Cited alongside, same era.
Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H. (2015) · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Hypernetworks
Ha, D., Dai, A., and Le, Q. V. (2017) · 2017
Later among the works it cites.
Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., and Courville, A. (2017) · 2017
Later among the works it cites.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
Later among the works it cites.
Behrmann, J., Duvenaud, D., and Jacobsen, J.-H. (2018) · 2018
Later among the works it cites.
Neural ordinary differential equations
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. (2018) · 2018
Later among the works it cites.
Neural autoregressive flows
Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A. (2018) · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015) · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 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) · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P. (2016) · 2016
Cited alongside, same era.
Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2017) · 2017
Cited alongside, same era.
UCI machine learning repository
Dua, D. and Karra Taniskidou, E. (2017) · 2017
Cited alongside, same era.
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P. (2018) · 2018
Later among the works it cites.
Transformation autoregressive networks
Oliva, J., Dubey, A., Zaheer, M., Poczos, B., Salakhutdinov, R., Xing, E., and Schneider, J. (2018) · 2018
Later among the works it cites.
Fundamentals of Probability and Stochastic Processes with Applications to Communications
Park, K. I. and Park (2018) · 2018
Later among the works it cites.
Sylvester normalizing flows for variational inference
van den Berg, R., Hasenclever, L., Tomczak, J. M., and Welling, M. (2018) · 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) · 2019
Closest in time.