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Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code.
Some methods for classification and analysis of multivariate observations
James MacQueen · 1967
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Auto-association by multilayer perceptrons and singular value decomposition
Hervé Bourlard and Yves Kamp · 1988
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The mnist database of handwritten digits
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Greedy layer-wise training of deep networks
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Discriminative clustering by regularized information maximization
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An analysis of single-layer networks in unsupervised feature learning
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Reading digits in natural images with unsupervised feature learning
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Better mixing via deep representations
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Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng · 2013
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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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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 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
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Ladder variational autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
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Improving variational auto-encoders using householder flow
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Conditional image generation with pixelcnn decoders
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Unsupervised deep embedding for clustering analysis
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Using artificial intelligence to augment human intelligence
Shan Carter and Michael Nielsen · 2017
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Learning discrete representations via information maximizing self augmented training
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
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Adversarially learned inference
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PixelVAE: A latent variable model for natural images
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Improved variational inference with inverse autoregressive flow
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Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Jakub M. Tomczak and Max Welling · 2017
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Neural discrete representation learning
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InfoVAE: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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A neural representation of sketch drawings
David Ha and Douglas Eck · 2018
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A hierarchical latent vector model for learning long-term structure in music
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Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
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