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Maximum Likelihood from Incomplete Data via the EM Algorithm
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Recent Trends in Hierarchic Document Clustering: A Critical Review
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Class-based N-gram Models of Natural Language
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Simple Statistical Gradient-following Algorithms for Connectionist Reinforcement Learning
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The Mathematics of Statistical Machine Translation: Parameter Estimation
Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993 · 1993
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Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights
Geoffrey E. Hinton and Drew van Camp. 1993 · 1993
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Tagging English Text with a Probabilistic Model
Bernard Merialdo. 1994 · 1994
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The Wake-Sleep Algorithm for Unsupervised Neural Networks
Geoffrey E. Hinton, Peter Dayan, Brendan J. Frey, and Radford M. Neal. 1995 · 1995
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Factorial Hidden Markov Models
Zoubin Ghahramani and Michael I. Jordan. 1996 · 1996
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Long Short-Term Memory
Sepp Hochreiter and Jurgen Schmidhuber. 1997 · 1997
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A New View of the EM Algorithm that Justifies Incremental, Sparse and Other Variants
Radford. M. Neal and Geoffrey E. Hinton. 1998 · 1998
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Introduction to Variational Methods for Graphical Models
Michael Jordan, Zoubin Ghahramani, Tommi Jaakkola, and Lawrence Saul. 1999 · 1999
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Annealed Importance Sampling
Radford M. Neal. 2001 · 2001
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A Neural Probabilistic Language Model
Yoshua Bengio, Rejean Ducharme, and Pascal Vincent. 2003 · 2003
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Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003
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Optimization with EM and Expectation-Conjugate-Gradient
Ruslan Salakhutdinov, Sam Roweis, and Zoubin Ghahramani. 2003 · 2003
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Integrating Topics and Syntax
Thomas L. Griffiths, Mark Steyvers, David M. Blei, and Joshua B. Tenenbaum. 2004 · 2004
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Corpus-based Induction of Syntactic Structure: Models of Dependency and Constituency
Dan Klein and Christopher D Manning. 2004 · 2004
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Contrastive Estimation: Training Log-Linear Models on Unlabeled Data
Noah A. Smith and Jason Eisner. 2005 · 2005
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Pattern Recognition and Machine Learning
Christopher M. Bishop. 2006 · 2006
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Gradient Estimation
Michael C. Fu. 2006 · 2006
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Prototype-driven Learning for Sequence Models
Aria Haghighi and Dan Klein. 2006 · 2006
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A Fast Learning Algorithm for Deep Belief Nets
Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. 2006 · 2006
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Why doesn’t EM find good HMM POS-taggers?
Mark Johnson. 2007 · 2007
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Syntactic Topic Models
Jordan L. Boyd-Graber and David M. Blei. 2008 · 2008
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A Bayesian LDA-based Model for Semi-Supervised Part-of-Speech Tagging
Kristina Toutanova and Mark Johnson. 2008 · 2008
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Introduction to Variational Methods for Graphical Models
Martin J. Wainwright and Michael I. Jordan. 2008 · 2008
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Improving Unsupervised Dependency Parsing with Richer Contexts and Smoothing
William P Headden III, Mark Johnson, and David McClosky. 2009 · 2009
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Painless Unsupervised Learning with Features
Taylor Berg-Kirkpatrick, Alexandre Bouchard-Cote, John DeNero, and Dan Klein. 2010 · 2010
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Two Decades of Unsupervised POS Induction: How far have we come?
Christos Christodoulopoulos, Sharon Goldwater, and Mark Steedman. 2010 · 2010
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Recurrent Neural Network Based Language Model
Tomas Mikolov, Martin Karafiat, Lukas Burget, Jan Cernocky, and Sanjeev Khudanpur. 2010 · 2010
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Efficient Learning of Deep Boltzmann Machines
Ruslan Salakhutdinov and Hugo Larochelle. 2010 · 2010
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Viterbi Training Improves Unsupervised Dependency Parsing
Valentin I Spitkovsky, Hiyan Alshawi, Daniel Jurafsky, and Christopher D Manning. 2010 · 2010
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A Hierarchical Pitman-Yor process HMM for Unsupervised Part of Speech Induction
Phil Blunsom and Trevor Cohn. 2011 · 2011
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
John Duchi, Elad Hazan, and Yoram Singer. 2011 · 2011
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The Neural Autoregressive Distribution Estimator
Hugo Larochelle and Iain Murray. 2011 · 2011
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Perturb-and-Map Random Fields: Using Discrete Optimization to Learn and Sample from Energy Models
George Papandreou and Alan L. Yuille. 2011 · 2011
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Unsupervised dependency parsing without gold part-of-speech tags
Valentin I Spitkovsky, Hiyan Alshawi, Angel X Chang, and Daniel Jurafsky. 2011 · 2011
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A Survey of Text Clustering Algorithms
Charu C. Aggarwal and ChengXiang Zhai. 2012 · 2012
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Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
Michael U. Gutmann and Aapo Hyvärinen. 2012 · 2012
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On the Partition Function and Random Maximum A-Posteriori Perturbation
Tamir Hazan and Tommi Jaakkola. 2012 · 2012
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Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors
Geoffrey Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2012 · 2012
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Approximate Bayesian Computational Methods
Jean-Michel Marin, Pierre Pudlo, Christian P. Robert, and Robin Ryder. 2012 · 2012
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Machine Learning: A Probabilistic Perspective
Kevin P. Murphy. 2012 · 2012
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An Introduction to Conditional Random Fields
Charles Sutton, Andrew McCallum, et al. 2012 · 2012
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Adadelta: An Adaptive Learning Rate Method
Matthew D. Zeiler. 2012 · 2012
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A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines
Kyunghyun Cho, Tapani Raiko, Alexander Ilin, and Juha Karhunen. 2013 · 2013
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Learning Factored Representations in a Deep Mixture of Experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever. 2013 · 2013
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Monte Carlo Methods in Financial Engineering , volume 53
Paul Glasserman. 2013 · 2013
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Stochastic Variational Inference
Matthew D. Hoffman, David M. Blei, Chong Wang, and John Paisley. 2013 · 2013
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Likelihood-free Inference via Classification
Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, and Jukka Corander. 2014 · 2014
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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Distributed Representations of Sentences and Documents
Quoc Le and Tomas Mikolov. 2014 · 2014
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A* Sampling
Chris J. Maddison, Daniel Tarlow, and Tom Minka. 2014 · 2014
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Neural Variational Inference and Learning in Belief Networks
Andryi Mnih and Karol Gregor. 2014 · 2014
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Black Box Variational Inference
Rajesh Ranganath, Sean Gerrish, and David M. Blei. 2014 · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo J. Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 2014
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Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, and Quoc Le. 2014 · 2014
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Recurrent Neural Network Regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014 · 2014
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Learning Wake-Sleep Recurrent Attention Models
Jimmy Ba, Ruslan R Salakhutdinov, Roger B Grosse, and Brendan J Frey. 2015 · 2015
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Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015 · 2015
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Reweighted Wake-Sleep
Jorg Bornschein and Yoshua Bengio. 2015 · 2015
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Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov. 2015 · 2015
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A Recurrent Latent Variable Model for Sequential Data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, and Yoshua Bengio. 2015 · 2015
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NICE: Non-linear Independent Components Estimation
Laurent Dinh, David Krueger, and Yoshua Bengio. 2015 · 2015
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MADE: Masked Autoencoder for Distribution Estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. 2015 · 2015
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Variational Dropout and the Local Reparameterization Trick
Durk P. Kingma, Tim Salimans, and Max Welling. 2015 · 2015
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Skip-thought Vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Unsupervised POS Induction with Word Embeddings
Chu-Cheng Lin, Waleed Ammar, Chris Dyer, and Lori Levin. 2015 · 2015
Masked Autoregressive Flow for Density Estimation
George Papamakarios, Theo Pavlakou, and Iain Murray. 2017 · 2017
Later among the works it cites.
Automatic Differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, and Lior Wolf. 2017 · 2017
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VAE Learning via Stein Variational Gradient Descent
Yunchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, and Lawrence Carin. 2017 · 2017
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Adversarial Generation of Natural Language
Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, and Aaron Courville. 2017 · 2017
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Cited alongside, same era.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2015 · 2015
Cited alongside, same era.
Variational Inference with Normalizing Flows
Danilo J. Rezende and Shakir Mohamed. 2015 · 2015
Cited alongside, same era.
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik Kingma, and Max Welling. 2015 · 2015
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Learning to Learn by Gradient Descent by Gradient Descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew Hoffman, David Pfau, Tom Schaul, and Nando de Freitas. 2016 · 2016
Cited alongside, same era.
Generating Sentences from a Continuous Space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyal, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul, Pedro A.M. Mediano, Marta Garnelo, Matthew C.H. Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan. 2016 · 2016
Cited alongside, same era.
Discrete Variational Autoencoders
Jason Tyler Rolfe. 2017 · 2017
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A Hybrid Convolutional Variational Autoencoder for Text Generation
Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth. 2017 · 2017
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A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017 · 2017
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
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Structured Variational Autoencoders for the Beta-Bernoulli Process
Rachit Singh, Jeffrey Ling, and Finale Doshi-Velez. 2017 · 2017
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REBAR: Low-variance, Unbiased Gradient Estimates for Discrete Latent Variable Models
George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, and Jascha Sohl-Dickstein. 2017 · 2017
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Attention is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Neural Response Generation via GAN with an Approximate Embedding Layer
Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang, Zhuoran Wang, and Chao Qi. 2017 · 2017
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Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick. 2017 · 2017
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Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data
Manzil Zaheer, Amr Ahmed, and Alexander J. Smola. 2017 · 2017
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Learning Hierarchical Features from Generative Models
Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2017 · 2017
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Fixing a Broken ELBO
Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, and Kevin Murphy. 2018 · 2018
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