Semi-Amortized Variational Autoencoders
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Alexander M. Rush | Yoon Kim | Sam Wiseman | Andrew C. Miller | David Sontag | D. Sontag | Yoon Kim | Sam Wiseman
[1] Hedvig Kjellström,et al. Advances in Variational Inference , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Ole Winther,et al. Ladder Variational Autoencoders , 2016, NIPS.
[3] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[4] Max Welling,et al. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.
[5] Benjamin Schrauwen,et al. Training energy-based models for time-series imputation , 2013, J. Mach. Learn. Res..
[6] Ryan P. Adams,et al. Composing graphical models with neural networks for structured representations and fast inference , 2016, NIPS.
[7] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[8] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[9] Barak A. Pearlmutter,et al. Automatic Learning Rate Maximization by On-Line Estimation of the Hessian's Eigenvectors , 1992, NIPS 1992.
[10] Eric P. Xing,et al. Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[11] Jason Tyler Rolfe,et al. Discrete Variational Autoencoders , 2016, ICLR.
[12] Uri Shalit,et al. Structured Inference Networks for Nonlinear State Space Models , 2016, AAAI.
[13] Alex Graves,et al. Decoupled Neural Interfaces using Synthetic Gradients , 2016, ICML.
[14] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[15] Mark W. Schmidt,et al. Minimizing finite sums with the stochastic average gradient , 2013, Mathematical Programming.
[16] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[17] David Vázquez,et al. PixelVAE: A Latent Variable Model for Natural Images , 2016, ICLR.
[18] Percy Liang,et al. Generating Sentences by Editing Prototypes , 2017, TACL.
[19] Phil Blunsom,et al. Discovering Discrete Latent Topics with Neural Variational Inference , 2017, ICML.
[20] Yisong Yue,et al. Iterative Amortized Inference , 2018, ICML.
[21] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[22] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[23] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[24] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[25] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[26] Juha Karhunen,et al. A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines , 2013, ICANN.
[27] Alexander M. Rush,et al. Structured Attention Networks , 2017, ICLR.
[28] Sean Gerrish,et al. Black Box Variational Inference , 2013, AISTATS.
[29] Justin Domke,et al. Generic Methods for Optimization-Based Modeling , 2012, AISTATS.
[30] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[31] Chong Wang,et al. TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency , 2016, ICLR.
[32] Tommi S. Jaakkola,et al. Sequence to Better Sequence: Continuous Revision of Combinatorial Structures , 2017, ICML.
[33] Ruslan Salakhutdinov,et al. Accurate and conservative estimates of MRF log-likelihood using reverse annealing , 2014, AISTATS.
[34] Yoshua Bengio,et al. Z-Forcing: Training Stochastic Recurrent Networks , 2017, NIPS.
[35] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[36] Yee Whye Teh,et al. Tighter Variational Bounds are Not Necessarily Better , 2018, ICML.
[37] Ryan P. Adams,et al. Gradient-based Hyperparameter Optimization through Reversible Learning , 2015, ICML.
[38] Dustin Tran,et al. Variational Gaussian Process , 2015, ICLR.
[39] Hugo Larochelle,et al. Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.
[40] Phil Blunsom,et al. Neural Variational Inference for Text Processing , 2015, ICML.
[41] Eric P. Xing,et al. Toward Controlled Generation of Text , 2017, ICML.
[42] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[43] Nebojsa Jojic,et al. Iterative Refinement of the Approximate Posterior for Directed Belief Networks , 2015, NIPS.
[44] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[45] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] J. Zico Kolter,et al. OptNet: Differentiable Optimization as a Layer in Neural Networks , 2017, ICML.
[47] Daan Wierstra,et al. Towards Conceptual Compression , 2016, NIPS.
[48] Joelle Pineau,et al. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues , 2016, AAAI.
[49] David Duvenaud,et al. Inference Suboptimality in Variational Autoencoders , 2018, ICML.
[50] Zoubin Ghahramani,et al. Propagation Algorithms for Variational Bayesian Learning , 2000, NIPS.
[51] Zhe Gan,et al. Topic Compositional Neural Language Model , 2017, AISTATS.
[52] Yisong Yue,et al. Learning to Infer , 2018, ICLR.
[53] Erhardt Barth,et al. A Hybrid Convolutional Variational Autoencoder for Text Generation , 2017, EMNLP.
[54] Andrew McCallum,et al. End-to-End Learning for Structured Prediction Energy Networks , 2017, ICML.
[55] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[56] Dustin Tran,et al. Hierarchical Variational Models , 2015, ICML.
[57] Stefano Ermon,et al. Towards Deeper Understanding of Variational Autoencoding Models , 2017, ArXiv.
[58] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[59] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[60] Arto Klami,et al. Importance Sampled Stochastic Optimization for Variational Inference , 2017, UAI.
[61] Matthew D. Hoffman,et al. On the challenges of learning with inference networks on sparse, high-dimensional data , 2017, AISTATS.
[62] Barak A. Pearlmutter. Fast Exact Multiplication by the Hessian , 1994, Neural Computation.
[63] Veselin Stoyanov,et al. Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure , 2011, AISTATS.
[64] Max Welling,et al. VAE with a VampPrior , 2017, AISTATS.
[65] Zhe Gan,et al. VAE Learning via Stein Variational Gradient Descent , 2017, NIPS.
[66] Ole Winther,et al. Sequential Neural Models with Stochastic Layers , 2016, NIPS.
[67] Pieter Abbeel,et al. Variational Lossy Autoencoder , 2016, ICLR.