An Introduction to Variational Autoencoders
暂无分享,去创建一个
[1] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[2] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[3] Surya Ganguli,et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics , 2015, ICML.
[4] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[5] Honglak Lee,et al. Attribute2Image: Conditional Image Generation from Visual Attributes , 2015, ECCV.
[6] Oriol Vinyals,et al. Bayesian Recurrent Neural Networks , 2017, ArXiv.
[7] Stefano Ermon,et al. Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models , 2017, AAAI.
[8] Shakir Mohamed,et al. Distribution Matching in Variational Inference , 2018, ArXiv.
[9] Alexandre Lacoste,et al. Neural Autoregressive Flows , 2018, ICML.
[10] Jakub M. Tomczak,et al. Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow , 2017, 1706.02326.
[11] Zhiting Hu,et al. Improved Variational Autoencoders for Text Modeling using Dilated Convolutions , 2017, ICML.
[12] Max Welling,et al. The Variational Fair Autoencoder , 2015, ICLR.
[13] Tom White,et al. Sampling Generative Networks: Notes on a Few Effective Techniques , 2016, ArXiv.
[14] David Duvenaud,et al. Reinterpreting Importance-Weighted Autoencoders , 2017, ICLR.
[15] Samy Bengio,et al. Density estimation using Real NVP , 2016, ICLR.
[16] Zhe Gan,et al. Variational Autoencoder for Deep Learning of Images, Labels and Captions , 2016, NIPS.
[17] Erhardt Barth,et al. A Hybrid Convolutional Variational Autoencoder for Text Generation , 2017, EMNLP.
[18] Radford M. Neal. MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.
[19] Max Welling,et al. Multiplicative Normalizing Flows for Variational Bayesian Neural Networks , 2017, ICML.
[20] Ole Winther,et al. Auxiliary Deep Generative Models , 2016, ICML.
[21] Max Welling,et al. Sylvester Normalizing Flows for Variational Inference , 2018, UAI.
[22] Jack P. C. Kleijnen,et al. Optimization and Sensitivity Analysis of Computer Simulation Models by the Score Function Method , 1996 .
[23] Sergey Levine,et al. MuProp: Unbiased Backpropagation for Stochastic Neural Networks , 2015, ICLR.
[24] Ole Winther,et al. Sequential Neural Models with Stochastic Layers , 2016, NIPS.
[25] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[26] Pieter Abbeel,et al. Variational Lossy Autoencoder , 2016, ICLR.
[27] Diederik P. Kingma,et al. Variational Recurrent Auto-Encoders , 2014, ICLR.
[28] H. Bourlard,et al. Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.
[29] Ole Winther,et al. Ladder Variational Autoencoders , 2016, NIPS.
[30] Aaron C. Courville,et al. Adversarially Learned Inference , 2016, ICLR.
[31] James T. Kwok,et al. Fast Second Order Stochastic Backpropagation for Variational Inference , 2015, NIPS.
[32] Ryan P. Adams,et al. Composing graphical models with neural networks for structured representations and fast inference , 2016, NIPS.
[33] Max Welling,et al. Improving Variational Auto-Encoders using Householder Flow , 2016, ArXiv.
[34] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[35] Miguel Lázaro-Gredilla,et al. Doubly Stochastic Variational Bayes for non-Conjugate Inference , 2014, ICML.
[36] Richard E. Turner,et al. Rényi Divergence Variational Inference , 2016, NIPS.
[37] Alex Graves,et al. Practical Variational Inference for Neural Networks , 2011, NIPS.
[38] David Vázquez,et al. PixelVAE: A Latent Variable Model for Natural Images , 2016, ICLR.
[39] Marc'Aurelio Ranzato,et al. Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.
[40] Arindam Banerjee,et al. An Analysis of Logistic Models: Exponential Family Connections and Online Performance , 2007, SDM.
[41] Aditya Deshpande,et al. Learning Diverse Image Colorization , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Geoffrey E. Hinton,et al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models , 2016, NIPS.
[43] Ole Winther,et al. How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks , 2016, ICML 2016.
[44] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[46] Michael I. Jordan,et al. Variational Bayesian Inference with Stochastic Search , 2012, ICML.
[47] Ariel D. Procaccia,et al. Variational Dropout and the Local Reparameterization Trick , 2015, NIPS.
[48] Maximilian Karl,et al. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data , 2016, ICLR.
[49] Max Welling,et al. Variational Graph Auto-Encoders , 2016, ArXiv.
[50] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[51] Gustavo Deco,et al. Higher Order Statistical Decorrelation without Information Loss , 1994, NIPS.
[52] Maxine Eskénazi,et al. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders , 2017, ACL.
[53] Thomas Brox,et al. Learning to generate chairs with convolutional neural networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[55] Julien Cornebise,et al. Weight Uncertainty in Neural Network , 2015, ICML.
[56] Roger B. Grosse,et al. Isolating Sources of Disentanglement in Variational Autoencoders , 2018, NeurIPS.
[57] Yoshua Bengio,et al. Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.
[58] Christian Osendorfer,et al. Learning Stochastic Recurrent Networks , 2014, NIPS 2014.
[59] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[60] Noah D. Goodman,et al. Amortized Inference in Probabilistic Reasoning , 2014, CogSci.
[61] Yoshua Bengio,et al. Bidirectional Helmholtz Machines , 2015, ICML.
[62] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[63] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[64] Martin A. Riedmiller,et al. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images , 2015, NIPS.
[65] Scott W. Linderman,et al. Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms , 2016, AISTATS.
[66] Dustin Tran,et al. Hierarchical Variational Models , 2015, ICML.
[67] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[68] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[69] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[70] Max Welling,et al. Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors , 2016, ICML.
[71] Hugo Larochelle,et al. MADE: Masked Autoencoder for Distribution Estimation , 2015, ICML.
[72] Tsao Yu,et al. Voice conversion from non-parallel corpora using variational auto-encoder , 2016 .
[73] Filip De Turck,et al. VIME: Variational Information Maximizing Exploration , 2016, NIPS.
[74] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[75] Ruslan Salakhutdinov,et al. Generating Images from Captions with Attention , 2015, ICLR.
[76] Max Welling,et al. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.
[77] Daan Wierstra,et al. Deep AutoRegressive Networks , 2013, ICML.
[78] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[79] Richard E. Turner,et al. Black-box α-divergence minimization , 2016, ICML 2016.
[80] Yu Tsao,et al. Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks , 2017, INTERSPEECH.
[81] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[82] Eric P. Xing,et al. Controllable Text Generation , 2017, ArXiv.
[83] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[84] Andriy Mnih,et al. Variational Inference for Monte Carlo Objectives , 2016, ICML.
[85] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[86] David Duvenaud,et al. Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference , 2017, ArXiv.
[87] Shakir Mohamed,et al. Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning , 2015, NIPS.
[88] Hugo Larochelle,et al. Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.
[89] Dustin Tran,et al. Deep Probabilistic Programming , 2017, ICLR.
[90] Max Welling,et al. Semi-supervised Learning with Deep Generative Models , 2014, NIPS.
[91] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[92] Sam T. Roweis,et al. EM Algorithms for PCA and SPCA , 1997, NIPS.
[93] Stefano Ermon,et al. Flow-GAN: Bridging implicit and prescribed learning in generative models , 2017, ArXiv.
[94] Uri Shalit,et al. Structured Inference Networks for Nonlinear State Space Models , 2016, AAAI.
[95] Tim Salimans,et al. A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features , 2016, ArXiv.
[96] Matt J. Kusner,et al. Grammar Variational Autoencoder , 2017, ICML.
[97] Ying Tan,et al. Variational Autoencoder for Semi-Supervised Text Classification , 2017, AAAI.
[98] Dmitry P. Vetrov,et al. Variational Dropout Sparsifies Deep Neural Networks , 2017, ICML.
[99] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[100] Joshua B. Tenenbaum,et al. Deep Convolutional Inverse Graphics Network , 2015, NIPS.
[101] David Wingate,et al. Automated Variational Inference in Probabilistic Programming , 2013, ArXiv.
[102] Sean Gerrish,et al. Black Box Variational Inference , 2013, AISTATS.
[103] Yoshua Bengio,et al. NICE: Non-linear Independent Components Estimation , 2014, ICLR.
[104] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[105] Amos J. Storkey,et al. Towards a Neural Statistician , 2016, ICLR.
[106] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[107] Dustin Tran,et al. Variational Gaussian Process , 2015, ICLR.
[108] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[109] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[110] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[111] David Silver,et al. Memory-based control with recurrent neural networks , 2015, ArXiv.
[112] Andrew Brock,et al. Neural Photo Editing with Introspective Adversarial Networks , 2016, ICLR.
[113] Daan Wierstra,et al. Towards Conceptual Compression , 2016, NIPS.
[114] Joelle Pineau,et al. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues , 2016, AAAI.
[115] Wojciech Zaremba,et al. An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.
[116] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[117] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[118] Iain Murray,et al. Masked Autoregressive Flow for Density Estimation , 2017, NIPS.
[119] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[120] Barnabás Póczos,et al. Enabling Dark Energy Science with Deep Generative Models of Galaxy Images , 2016, AAAI.
[121] Daan Wierstra,et al. One-Shot Generalization in Deep Generative Models , 2016, ICML.
[122] Navdeep Jaitly,et al. Adversarial Autoencoders , 2015, ArXiv.
[123] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[124] David M. Blei,et al. The Generalized Reparameterization Gradient , 2016, NIPS.
[125] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[126] Max Jaderberg,et al. Unsupervised Learning of 3D Structure from Images , 2016, NIPS.
[127] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[128] Paul Glasserman,et al. Monte Carlo Methods in Financial Engineering , 2003 .
[129] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[130] Demis Hassabis,et al. Neural Episodic Control , 2017, ICML.
[131] Tim Salimans,et al. Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression , 2012, ArXiv.
[132] Max Welling,et al. Bayesian Compression for Deep Learning , 2017, NIPS.
[133] Zoubin Ghahramani,et al. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.
[134] Ziwei Liu,et al. Semantic Facial Expression Editing using Autoencoded Flow , 2016, ArXiv.
[135] Ralph Linsker,et al. An Application of the Principle of Maximum Information Preservation to Linear Systems , 1988, NIPS.
[136] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[137] Phil Blunsom,et al. Neural Variational Inference for Text Processing , 2015, ICML.
[138] Yuval Tassa,et al. Learning Continuous Control Policies by Stochastic Value Gradients , 2015, NIPS.
[139] Peter W. Glynn,et al. Likelihood ratio gradient estimation for stochastic systems , 1990, CACM.
[140] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[141] David Duvenaud,et al. Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference , 2017, NIPS.
[142] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[143] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[144] Dustin Tran,et al. Automatic Differentiation Variational Inference , 2016, J. Mach. Learn. Res..