Fetching the paper…
Reading the bibliography…
Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference.
An introduction to differentiable manifolds and Riemannian geometry , volume 120
Boothby, W. M · 1986
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F · 1990
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
On sufficient conditions of the injectivity: development of a numerical test algorithm via interval analysis
Lagrange, S., Delanoue, N., and Jaulin, L · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
What regularized auto-encoders learn from the data-generating distribution
Alain, G. 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.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
Earlier work this paper cites.
Analyzing noise in autoencoders and deep networks, 2014
Poole, B., Sohl-Dickstein, J., and Ganguli, S · 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.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
Earlier work this paper cites.
Nonlinear programming: 3rd edition
Bertsekas, D. P · 2016
Earlier work this paper cites.
Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
Cited alongside, same era.
Rényi divergence variational inference
Li, Y. and Turner, R. E · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
Cited alongside, same era.
Learning disentangled representations with semi-supervised deep generative models
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
Later among the works it cites.
Variational inference of disentangled latent concepts from unlabeled observations
Kumar, A., Sattigeri, P., and Balakrishnan, A · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
Later among the works it cites.
Is generator conditioning causally related to gan performance?
Odena, A., Buckman, J., Olsson, C., Brown, T. B., Olah, C., Raffel, C., and Goodfellow, I · 2018
Later among the works it cites.
Rezende, D. J. and Viola, F · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Narayanaswamy, S., Paige, T. B., Van de Meent, J.-W., Desmaison, A., Goodman, N., Kohli, P., Wood, F., and Torr, P · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Cited alongside, same era.
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2017
Cited alongside, same era.
Neural discrete representation learning
van den Oord, A., Vinyals, O., et al · 2017
Cited alongside, same era.
Invertible residual networks, 2018
Behrmann, J., Grathwohl, W., Chen, R. T. Q., Duvenaud, D., and Jacobsen, J.-H · 2018
Cited alongside, same era.
Understanding and improving interpolation in autoencoders via an adversarial regularizer
Berthelot, D., Raffel, C., Roy, A., and Goodfellow, I · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, T. Q., Li, X., Grosse, R. B., and Duvenaud, D. K · 2018
Cited alongside, same era.
Variational noise-contrastive estimation
Rhodes, B. and Gutmann, M · 2018
Later among the works it cites.
Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
Later among the works it cites.
Diagnosing and enhancing vae models
Dai, B. and Wipf, D · 2019
Later among the works it cites.
From variational to deterministic autoencoders, 2019
Ghosh, P., Sajjadi, M. S. M., Vergari, A., Black, M., and Schölkopf, B · 2019
Later among the works it cites.
Understanding the (un) interpretability of natural image distributions using generative models
Krusinga, R., Shah, S., Zwicker, M., Goldstein, T., and Jacobs, D · 2019
Later among the works it cites.
On the quantitative analysis of decoder-based generative models
Wu, Y., Burda, Y., Salakhutdinov, R., and Grosse, R · 2019
Later among the works it cites.
On the invertibility of invertible neural networks, 2020
Behrmann, J., Vicol, P., Wang, K.-C., Grosse, R. B., and Jacobsen, J.-H · 2020
Closest in time.
On implicit regularization in β \beta -vaes
Kumar, A. and Poole, B · 2020
Closest in time.