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In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.
Disentangled state space representations
Đorđe Miladinović, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M Buhmann, and Stefan Bauer · 1906
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Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
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Building a large annotated corpus of English: The Penn Treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz · 2004
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Search-based structured prediction
Hal Daumé, John Langford, and Daniel Marcu · 2009
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Rao-blackwellizing the straight-through gumbel-softmax gradient estimator
Max B Paulus, Chris J Maddison, and Andreas Krause · 2010
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Auto-encoding variational bayes
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Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
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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
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Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2015
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio · 2016
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Domain-adversarial training of neural networks
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Conditional image generation with pixelcnn decoders
Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu · 2016
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Efficient and flexible inference for stochastic systems
Stefan Bauer, Nico S Gorbach, Djordje Miladinovic, and Joachim M Buhmann · 2017
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Variational lossy autoencoder
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
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Concrete dropout
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Variational dropout sparsifies deep neural networks
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A hybrid convolutional variational autoencoder for text generation
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Cyclical annealing schedule: A simple approach to mitigating KL vanishing
Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, and Lawrence Carin · 2019
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Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
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A surprisingly effective fix for deep latent variable modeling of text
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An exponential learning rate schedule for deep learning
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Don’t blame the elbo! a linear vae perspective on posterior collapse
James Lucas, George Tucker, Roger B Grosse, and Mohammad Norouzi · 2019
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Improved variational autoencoders for text modeling using dilated convolutions
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Information dropout: Learning optimal representations through noisy computation
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Stochastic variational video prediction
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Semi-amortized variational autoencoders
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Rapid and reversible control of human metabolism by individual sleep states
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