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Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models.
Latent normalizing flows for discrete sequences
Zachary M. Ziegler and Alexander M. Rush. 2019 · 1901
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Srilm – an extensible language modeling toolkit
Andreas Stolcke. 2002 · 2002
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Shallow parsing with conditional random fields
Fei Sha and Fernando Pereira. 2003 · 2003
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Efficient inference in fully connected crfs with gaussian edge potentials
Philipp Krähenbühl and Vladlen Koltun. 2012 · 2012
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Learning graphical model parameters with approximate marginal inference
Justin Domke. 2013 · 2013
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Generating sequences with recurrent neural networks
Alex Graves. 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Stochastic back-propagation and variational inference in deep latent gaussian models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
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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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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Globally normalized transition-based neural networks
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016 · 2016
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016 · 2016
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Sequential neural models with stochastic layers
Marco Fraccaro, Søren Kaae Sø nderby, Ulrich Paquet, and Ole Winther. 2016 · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
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Professor forcing: A new algorithm for training recurrent networks
Anirudh Goyal, Alex Lamb, Ying Zhang, Saizheng Zhang, Aaron C. Courville, and Yoshua Bengio. 2016 · 2016
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Z-forcing: Training stochastic recurrent networks
Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, and Yoshua Bengio. 2017 · 2017
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Bidirectional LSTM-CRF models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2017 · 2017
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Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. 2017 · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
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Diederik P. Kingma, Tim Salimans, and Max Welling. 2016 · 2016
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard H. Hovy. 2016 · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
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Self-critical sequence training for image captioning
Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2016 · 2016
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Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C Courville, and Joelle Pineau. 2016 · 2016
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Neural machine translation with reconstruction
Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, and Hang Li. 2016 · 2016
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Sequence-to-sequence learning as beam-search optimization
Sam Wiseman and Alexander M. Rush. 2016 · 2016
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Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017 · 2017
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Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin. 2018 · 2018
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Maskgan: Better text generation via filling in the ______
William Fedus, Ian J. Goodfellow, and Andrew M. Dai. 2018 · 2018
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SEARNN: training rnns with global-local losses
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, and Simon Lacoste-Julien. 2018 · 2018
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Deterministic non-autoregressive neural sequence modeling by iterative refinement
Jason Lee, Elman Mansimov, and Kyunghyun Cho. 2018 · 2018
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Controlling global statistics in recurrent neural network text generation
Thanapon Noraset, David Demeter, and Doug Downey. 2018 · 2018
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Deep state space models for unconditional word generation
Florian Schmidt and Thomas Hofmann. 2018 · 2018
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On accurate evaluation of gans for language generation
Stanislau Semeniuta, Aliaksei Severyn, and Sylvain Gelly. 2018 · 2018
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Jingjing Xu, Xu Sun, Xuancheng Ren, Junyang Lin, Bingzhen Wei, and Wei Li. 2018 · 2018
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Advances in variational inference
Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, and Stephan Mandt. 2018 · 2018
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