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Samy Bengio | Jascha Sohl-Dickstein | Laurent Dinh | Samy Bengio | Laurent Dinh | Jascha Narain Sohl-Dickstein
[1] Gustavo Deco,et al. Higher Order Statistical Decorrelation without Information Loss , 1994, NIPS.
[2] Tim Salimans,et al. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.
[3] Juha Karhunen,et al. An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models , 2002, Neural Computation.
[4] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[5] Joan Bruna,et al. Super-Resolution with Deep Convolutional Sufficient Statistics , 2015, ICLR.
[6] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[7] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[8] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[9] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[10] Yoshua Bengio,et al. Artificial neural networks and their application to sequence recognition , 1991 .
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Leon A. Gatys,et al. Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.
[13] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[14] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[15] Aapo Hyvärinen,et al. Nonlinear independent component analysis: Existence and uniqueness results , 1999, Neural Networks.
[16] Martin A. Riedmiller,et al. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images , 2015, NIPS.
[17] David Silver,et al. Learning functions across many orders of magnitudes , 2016, ArXiv.
[18] Surya Ganguli,et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics , 2015, ICML.
[19] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[20] Ole Winther,et al. Auxiliary Deep Generative Models , 2016, ICML.
[21] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[22] Ryan P. Adams,et al. High-Dimensional Probability Estimation with Deep Density Models , 2013, ArXiv.
[23] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[24] Yonghui Wu,et al. Exploring the Limits of Language Modeling , 2016, ArXiv.
[25] Daan Wierstra,et al. Towards Conceptual Compression , 2016, NIPS.
[26] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[27] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[28] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[29] David Silver,et al. Learning values across many orders of magnitude , 2016, NIPS.
[30] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[31] Luc Devroye,et al. Sample-based non-uniform random variate generation , 1986, WSC '86.
[32] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[33] Zhuowen Tu,et al. Deeply-Supervised Nets , 2014, AISTATS.
[34] Hugo Larochelle,et al. RNADE: The real-valued neural autoregressive density-estimator , 2013, NIPS.
[35] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[36] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[37] Hugo Larochelle,et al. The Neural Autoregressive Distribution Estimator , 2011, AISTATS.
[38] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[39] Matthias Bethge,et al. Generative Image Modeling Using Spatial LSTMs , 2015, NIPS.
[40] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[41] Matthias Bethge,et al. A note on the evaluation of generative models , 2015, ICLR.
[42] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[43] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[44] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[45] Samy Bengio,et al. Order Matters: Sequence to sequence for sets , 2015, ICLR.
[46] Dustin Tran,et al. Variational Gaussian Process , 2015, ICLR.
[47] Valero Laparra,et al. Density Modeling of Images using a Generalized Normalization Transformation , 2015, ICLR.
[48] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[49] Samy Bengio,et al. Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks , 1999, NIPS.
[50] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[51] Roberto Cipolla,et al. Understanding symmetries in deep networks , 2015, ArXiv.
[52] Tapani Raiko,et al. Stochastic gradient estimate variance in contrastive divergence and persistent contrastive divergence , 2016, ESANN.
[53] Hui Jiang,et al. Generating images with recurrent adversarial networks , 2016, ArXiv.
[54] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[55] Hugo Larochelle,et al. MADE: Masked Autoencoder for Distribution Estimation , 2015, ICML.
[56] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[57] Yoshua Bengio,et al. NICE: Non-linear Independent Components Estimation , 2014, ICLR.
[58] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[59] Diogo Almeida,et al. Resnet in Resnet: Generalizing Residual Architectures , 2016, ArXiv.
[60] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[61] Max Welling,et al. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.
[62] Michael I. Jordan,et al. Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..
[63] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[64] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[65] Alexei A. Efros,et al. Colorful Image Colorization , 2016, ECCV.
[66] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[67] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[68] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[69] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[70] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[71] Sergey Levine,et al. Continuous Deep Q-Learning with Model-based Acceleration , 2016, ICML.
[72] Seungjin Choi,et al. Independent Component Analysis , 2009, Handbook of Natural Computing.
[73] Brendan J. Frey,et al. Graphical Models for Machine Learning and Digital Communication , 1998 .