Fetching the paper…
Reading the bibliography…
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.
Learning with local and global consistency
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Schölkopf, B · 2004
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 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.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M · 2014
Earlier work this paper cites.
Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
Earlier work this paper cites.
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Oord, A. v. d., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Earlier work this paper cites.
Good semi-supervised learning that requires a bad GAN
Dai, Z., Yang, Z., Yang, F., Cohen, W. W., and Salakhutdinov, R. R · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Feature visualization
Olah, C., Mordvintsev, A., and Schubert, L · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
Cited alongside, same era.
Variational autoencoder for semi-supervised text classification
Xu, W., Sun, H., Deng, C., and Tan, Y · 2017
Realistic evaluation of deep semi-supervised learning algorithms
Oliver, A., Odena, A., Raffel, C. A., Cubuk, E. D., and Goodfellow, I · 2018
Later among the works it cites.
Semi-conditional normalizing flows for semi-supervised learning
Atanov, A., Volokhova, A., Ashukha, A., Sosnovik, I., and Vetrov, D · 2019
Closest in time.
There are many consistent explanations of unlabeled data: Why you should average
Athiwaratkun, B., Finzi, M., Izmailov, P., and Wilson, A. G · 2019
Closest in time.
MixMatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C · 2019
Closest in time.
Residual flows for invertible generative modeling
Chen, R. T., Behrmann, J., Duvenaud, D., and Jacobsen, J.-H · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Behrmann, J., Duvenaud, D., and Jacobsen, J.-H · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1 × \times 1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Ishii, S., and Koyama, M · 2018
Cited alongside, same era.
Foundations of machine learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A · 2018
Cited alongside, same era.
Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
Cited alongside, same era.
Closest in time.
Invertible convolutional networks
Finzi, M., Izmailov, P., Maddox, W., Kirichenko, P., and Wilson, A. G · 2019
Closest in time.
Semi-supervised learning with normalizing flows
Izmailov, P., Kirichenko, P., Finzi, M., and Wilson, A. G · 2019
Closest in time.
Hybrid models with deep and invertible features
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2019
Closest in time.
Mintnet: Building invertible neural networks with masked convolutions
Song, Y., Meng, C., and Ermon, S · 2019
Closest in time.
Interpolation consistency training for semi-supervised learning
Verma, V., Lamb, A., Kannala, J., Bengio, Y., and Lopez-Paz, D · 2019
Closest in time.
Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
Berthelot, D., Carlini, N., Cubuk, E. D., Kurakin, A., Sohn, K., Zhang, H., and Raffel, C · 2020
Closest in time.
Unsupervised data augmentation for consistency training, 2020
Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., and Le, Q. V · 2020
Closest in time.