f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
暂无分享,去创建一个
Sebastian Nowozin | Ryota Tomioka | Botond Cseke | Ryota Tomioka | S. Nowozin | Botond Cseke | Sebastian Nowozin
[1] S. M. Ali,et al. A General Class of Coefficients of Divergence of One Distribution from Another , 1966 .
[2] S. Srihari. Mixture Density Networks , 1994 .
[3] D. Mackay,et al. Bayesian neural networks and density networks , 1995 .
[4] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[5] Imre Csiszár,et al. Information Theory and Statistics: A Tutorial , 2004, Found. Trends Commun. Inf. Theory.
[6] Thomas P. Minka,et al. Divergence measures and message passing , 2005 .
[7] Igor Vajda,et al. On Divergences and Informations in Statistics and Information Theory , 2006, IEEE Transactions on Information Theory.
[8] Le Song,et al. A Kernel Statistical Test of Independence , 2007, NIPS.
[9] A. Raftery,et al. Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .
[10] Martin J. Wainwright,et al. Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization , 2008, IEEE Transactions on Information Theory.
[11] Aapo Hyvärinen,et al. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.
[12] Bernhard Schölkopf,et al. Hilbert Space Embeddings and Metrics on Probability Measures , 2009, J. Mach. Learn. Res..
[13] Mark D. Reid,et al. Information, Divergence and Risk for Binary Experiments , 2009, J. Mach. Learn. Res..
[14] Hugo Larochelle,et al. The Neural Autoregressive Distribution Estimator , 2011, AISTATS.
[15] Hugo Larochelle,et al. RNADE: The real-valued neural autoregressive density-estimator , 2013, NIPS.
[16] Alex Graves,et al. Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.
[17] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[18] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[19] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[20] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[21] Surya Ganguli,et al. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization , 2014, NIPS.
[22] Frank Nielsen,et al. On the chi square and higher-order chi distances for approximating f-divergences , 2013, IEEE Signal Processing Letters.
[23] Ferenc Huszar,et al. How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? , 2015, ArXiv.
[24] Surya Ganguli,et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics , 2015, ICML.
[25] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[26] Zoubin Ghahramani,et al. Training generative neural networks via Maximum Mean Discrepancy optimization , 2015, UAI.
[27] Jon Gauthier. Conditional generative adversarial nets for convolutional face generation , 2015 .
[28] Kenta Oono,et al. Chainer : a Next-Generation Open Source Framework for Deep Learning , 2015 .
[29] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[30] Ian J. Goodfellow,et al. On distinguishability criteria for estimating generative models , 2014, ICLR.
[31] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[32] Navdeep Jaitly,et al. Adversarial Autoencoders , 2015, ArXiv.
[33] Matthias Bethge,et al. A note on the evaluation of generative models , 2015, ICLR.
[34] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[35] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[36] Leon Hirsch,et al. Fundamentals Of Convex Analysis , 2016 .
[37] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.