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Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood.
Use of different monte carlo sampling techniques
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A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
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Generalization of back-propagation to recurrent neural networks
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Particle transport and image synthesis
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Unsupervised feature selection for principal components analysis
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A general method for debiasing a monte carlo estimator
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Matrix analysis
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A new approach to unbiased estimation for sde’s
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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
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Unbiased estimation with square root convergence for sde models
Chang-han Rhee and Peter W Glynn · 2015
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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QETLAB: A MATLAB toolbox for quantum entanglement, version 0.9
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Regularisation of neural networks by enforcing lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael Cree · 2018
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Stochastic chebyshev gradient descent for spectral optimization
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Reviving and improving recurrent back-propagation
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, and Richard Zemel · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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