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Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning.
Matrix Computations (3rd Ed.)
Golub, G. H. and Van Loan, C. F · 1996
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.
Making things happen: A theory of causal explanation
Woodward, J · 2005
Earlier work this paper cites.
Interventions and causal inference
Eberhardt, F. and Scheines, R · 2007
Earlier work this paper cites.
Causal reasoning through intervention
Hagmayer, Y., Sloman, S. A., Lagnado, D. A., and Waldmann, M. R · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Causality
Pearl, J · 2009
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
Earlier work this paper cites.
Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y · 2011
Earlier work this paper cites.
Unsupervised feature learning and deep learning: A review and new perspectives
Bengio, Y., Courville, A. C., and Vincent, P · 2012
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 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 Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
Earlier work this paper cites.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E. L., Chintala, S., Szlam, A., and Fergus, R · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., and Krishnan, D · 2016
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Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W · 2016
Cited alongside, same era.
Comparison of maximum likelihood and gan-based training of real nvps
Danihelka, I., Lakshminarayanan, B., Uria, B., Wierstra, D., and Dayan, P · 2017
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NIPS 2016 Tutorial: Generative Adversarial Networks
Goodfellow, I · 2017
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Flow-gan: Bridging implicit and prescribed learning in generative models
Grover, A., Dhar, M., and Ermon, S · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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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
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Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2016
Cited alongside, same era.
Conditional Image Synthesis With Auxiliary Classifier GANs
Odena, A., Olah, C., and Shlens, J · 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.
On the quantitative analysis of decoder-based generative models
Wu, Y., Burda, Y., Salakhutdinov, R., and Grosse, R. B · 2016
Cited alongside, same era.
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D · 2016
Cited alongside, same era.
Towards Principled Methods for Training Generative Adversarial Networks
Arjovsky, M. and Bottou, L · 2017
Cited alongside, same era.
Wasserstein GAN
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Do gans actually learn the distribution? an empirical study
Arora, S. and Zhang, Y · 2017
Cited alongside, same era.
Learning deep latent gaussian models with markov chain monte carlo
Hoffman, M. D · 2017
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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Improved Semi-supervised Learning with GANs using Manifold Invariances
Kumar, A., Sattigeri, P., and Fletcher, P. T · 2017
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Gradient descent GAN optimization is locally stable
Nagarajan, V. and Kolter, J. Z · 2017
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Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Pennington, J., Schoenholz, S., and Ganguli, S · 2017
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The Riemannian Geometry of Deep Generative Models
Shao, H., Kumar, A., and Fletcher, P. T · 2017
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
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cGANs with projection discriminator
Miyato, T. and Koyama, M · 2018
Closest in time.
Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Sensitivity and generalization in neural networks: an empirical study
Novak, R., Bahri, Y., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J · 2018
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Self-attention generative adversarial networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A · 2018
Closest in time.