Fetching the paper…
Reading the bibliography…
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 1902
Earlier work this paper cites.
An information-maximization approach to blind separation and blind deconvolution
Bell, A. J. and Sejnowski, T. J · 1995
Earlier work this paper cites.
Higher order statistical decorrelation without information loss
Deco, G. and Brauer, W · 1995
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P · 1999
Earlier work this paper cites.
Independent component analysis , volume 46
Hyvärinen, A., Karhunen, J., and Oja, E · 2004
Earlier work this paper cites.
The Neural Autoregressive Distribution Estimator
Larochelle, H. and Murray, I · 2011
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
Uria, B., Murray, I., and Larochelle, H · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
Cited alongside, same era.
Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Cited alongside, same era.
A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
Cited alongside, same era.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Cited alongside, same era.
Multiplicative normalizing flows for variational bayesian neural networks
Louizos, C. and Welling, M · 2017
Later among the works it cites.
Fast generation for convolutional autoregressive models
Ramachandran, P., Paine, T. L., Khorrami, P., Babaeizadeh, M., Chang, S., Zhang, Y., Hasegawa-Johnson, M. A., Campbell, R. H., and Huang, T. S · 2017
Later among the works it cites.
Parallel multiscale autoregressive density estimation
Reed, S. E., van den Oord, A., Kalchbrenner, N., Gómez, S., Wang, Z., Belov, D., and de Freitas, N · 2017
Later among the works it cites.
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
Later among the works it cites.
Neural autoregressive flows
Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Language modeling with gated convolutional networks
Dauphin, Y. N., Fan, A., Auli, M., and Grangier, D · 2016
Cited alongside, same era.
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
Cited alongside, same era.
Improving variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., and Welling, M · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P · 2016
Cited alongside, same era.
Pixelsnail: An improved autoregressive generative model
Chen, X., Mishra, N., Rohaninejad, M., and Abbeel, P · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
Cited alongside, same era.
eural machine translation in linear time
Kalchbrenner, N., Espheholt, L., Simonyan, K., Oord, A. v. d., Graves, A., and Kavukcuoglu, K
Cited in the paper.
Later among the works it cites.
Efficient neural audio synthesis
Kalchbrenner, N., Elsen, E., Simonyan, K., Noury, S., Casagrande, N., Lockhart, E., Stimberg, F., Oord, A. v. d., Dieleman, S., and Kavukcuoglu, K · 2018
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
Later among the works it cites.
A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P · 2018
Later among the works it cites.
Müller, T., McWilliams, B., Rousselle, F., Gross, M., and Novák, J · 2018
Later among the works it cites.
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., and Ku, A · 2018
Later among the works it cites.