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We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression.
Distribution of quantiles in samples from a bivariate population
Siddiqui, M. M · 1960
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Robust estimation of a location parameter
Huber, P. J · 1964
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Which part of the sample contains the information?
Tukey, J. W · 1965
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Nonparametric statistical data modeling
Parzen, E · 1979
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Estimating densities, quantiles, quantile densities and density quantiles
Jones, M. C · 1992
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Acceleration of stochastic approximation by averaging
Polyak, B. T. and Juditsky, A. B · 1992
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Confidence intervals for regression quantiles
Koenker, R · 1994
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Quantile regression: an introduction
Koenker, R. and Hallock, K · 2001
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Risk measures and comonotonicity: a review
Dhaene, J., Vanduffel, S., Goovaerts, M. J., Kaas, R., Tang, Q., and Vyncke, D · 2006
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Quantile autoregression
Koenker, R. and Xiao, Z · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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MADE: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
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Radford, A., Metz, L., and Chintala, S · 2015
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Imagenet large scale visual recognition challenge
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PixelSNAIL: An improved autoregressive generative model
Chen, X., Mishra, N., Rohaninejad, M., and Abbeel, P · 2017
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Daskalakis, C., Ilyas, A., Syrgkanis, V., and Zeng, H · 2017
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GAN and VAE from an optimal transport point of view
Genevay, A., Peyré, G., and Cuturi, M · 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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Theis, L., van den Oord, A., and Bethge, M · 2015
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Carlier, G., Chernozhukov, V., and Galichon, A · 2016
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., and Courville, A · 2016
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Multiple-output quantile regression
Hallin, M. and Miroslav, Š · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Progressive growing of GANs for improved quality, stability, and variation
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Variational approaches for auto-encoding generative adversarial networks
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