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Aaron C. Courville | Alex Lamb | Vincent Dumoulin | Ben Poole | Martín Arjovsky | Olivier Mastropietro | Ishmael Belghazi | Ben Poole | Vincent Dumoulin | Alex Lamb | Olivier Mastropietro | Martín Arjovsky | Ishmael Belghazi
[1] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[2] Andrew Brock,et al. Neural Photo Editing with Introspective Adversarial Networks , 2016, ICLR.
[3] Yoshua Bengio,et al. Maxout Networks , 2013, ICML.
[4] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[5] Thomas Brox,et al. Generating Images with Perceptual Similarity Metrics based on Deep Networks , 2016, NIPS.
[6] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[7] Tapani Raiko,et al. Semi-supervised Learning with Ladder Networks , 2015, NIPS.
[8] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[9] Yoshua Bengio,et al. Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.
[10] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[11] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[12] Aaron C. Courville,et al. Discriminative Regularization for Generative Models , 2016, ArXiv.
[13] Matthias Bethge,et al. A note on the evaluation of generative models , 2015, ICLR.
[14] Diederik P. Kingma. Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form , 2013, ArXiv.
[15] Navdeep Jaitly,et al. Adversarial Autoencoders , 2015, ArXiv.
[16] Xinyun Chen. Under Review as a Conference Paper at Iclr 2017 Delving into Transferable Adversarial Ex- Amples and Black-box Attacks , 2016 .
[17] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[18] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[19] Trevor Darrell,et al. Adversarial Feature Learning , 2016, ICLR.
[20] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[21] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[22] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[23] Max Welling,et al. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.
[24] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[25] Vincent Dumoulin,et al. Deconvolution and Checkerboard Artifacts , 2016 .
[26] Ole Winther,et al. Auxiliary Deep Generative Models , 2016, ICML.
[27] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[28] Francesco Visin,et al. A guide to convolution arithmetic for deep learning , 2016, ArXiv.
[29] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[30] Jost Tobias Springenberg,et al. Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks , 2015, ICLR.
[31] Max Welling,et al. Semi-supervised Learning with Deep Generative Models , 2014, NIPS.
[32] Yoshua Bengio,et al. Blocks and Fuel: Frameworks for deep learning , 2015, ArXiv.
[33] Yoshua Bengio,et al. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation , 2013, ArXiv.
[34] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[35] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[36] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[37] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[38] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[39] Razvan Pascanu,et al. Theano: new features and speed improvements , 2012, ArXiv.
[40] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[41] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[42] Christian Ledig,et al. Is the deconvolution layer the same as a convolutional layer? , 2016, ArXiv.
[43] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[44] Yann LeCun,et al. Stacked What-Where Auto-encoders , 2015, ArXiv.