AdaGAN: Boosting Generative Models
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Bernhard Schölkopf | Sylvain Gelly | Olivier Bousquet | Ilya O. Tolstikhin | Carl-Johann Simon-Gabriel | B. Schölkopf | O. Bousquet | S. Gelly | I. Tolstikhin | Carl-Johann Simon-Gabriel | B. Scholkopf
[1] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[2] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[3] Andrew R. Barron,et al. Mixture Density Estimation , 1999, NIPS.
[4] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[5] Geoffrey E. Hinton,et al. Self Supervised Boosting , 2002, NIPS.
[6] Saharon Rosset,et al. Boosting Density Estimation , 2002, NIPS.
[7] Flemming Topsøe,et al. Jensen-Shannon divergence and Hilbert space embedding , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..
[8] Matthias Hein,et al. Hilbertian Metrics and Positive Definite Kernels on Probability Measures , 2005, AISTATS.
[9] Zhuowen Tu,et al. Learning Generative Models via Discriminative Approaches , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[10] Mark D. Reid,et al. Information, Divergence and Risk for Binary Experiments , 2009, J. Mach. Learn. Res..
[11] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[12] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[13] W. H. Pun,et al. Statistical Decision Theory , 2014 .
[14] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[15] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[16] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[17] Joost van de Weijer,et al. Ensembles of Generative Adversarial Networks , 2016, ArXiv.
[18] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[19] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[20] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[21] Yoshua Bengio,et al. Mode Regularized Generative Adversarial Networks , 2016, ICLR.
[22] Stefano Ermon,et al. Boosted Generative Models , 2016, AAAI.