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Learning a generative model from partial data (data with missingness) is a challenging area of machine learning research.
Handling missing values in support vector machine classifiers
Kristiaan Pelckmans, Jos De Brabanter, Johan AK Suykens, and Bart De Moor · 2005
Earlier work this paper cites.
Efficient em training of gaussian mixtures with missing data
Olivier Delalleau, Aaron Courville, and Yoshua Bengio · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Covarep: A collaborative voice analysis repository for speech technologies
Gilles Degottex, John Kane, Thomas Drugman, Tuomo Raitio, and Stefan Scherer · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Principal component analysis with missing values: a comparative survey of methods
Stéphane Dray and Julie Josse · 2015
Earlier work this paper cites.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Learning to generate samples from noise through infusion training
Florian Bordes, Sina Honari, and Pascal Vincent · 2017
Cited alongside, same era.
Joseph Marino, Yisong Yue, and Stephan Mandt · 2018
Later among the works it cites.
Variational autoencoders for missing data imputation with application to a simulated milling circuit
John T McCoy, Steve Kroon, and Lidia Auret · 2018
Later among the works it cites.
Handling incomplete heterogeneous data using vaes
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera · 2018
Later among the works it cites.
Christopher KI Williams, Charlie Nash, and Alfredo Nazábal · 2018
Later among the works it cites.
Gain: Missing data imputation using generative adversarial nets
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High-resolution image inpainting using multi-scale neural patch synthesis
Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, and Hao Li · 2017
Cited alongside, same era.
The menpo facial landmark localisation challenge: A step towards the solution
Stefanos Zafeiriou, George Trigeorgis, Grigorios Chrysos, Jiankang Deng, and Jie Shen · 2017
Cited alongside, same era.
Variational autoencoder with arbitrary conditioning
Oleg Ivanov, Michael Figurnov, and Dmitry Vetrov · 2018
Cited alongside, same era.
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2018
Later among the works it cites.
Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph
AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency · 2018
Later among the works it cites.
Pseudo-encoded stochastic variational inference
Amir Zadeh, Smon Hessner, Yao-Chong Lim, and Louis-Phlippe Morency · 2019
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