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An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited.
Learning internal representations by error propagation
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Sparse autoencoder
Andrew Ng · 2000
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Sparse coding of natural images using an overcomplete set of limited capacity units
Eizaburo Doi and Michael S. Lewicki · 2005
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Sparse feature learning for deep belief networks
Marc’Aurelio Ranzato, Y-Lan Boureau, and Yann LeCun · 2007
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Visualizing data using t-sne
L. V. D. Maaten and Geoffrey E. Hinton · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2013
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Saturating auto-encoders
Rotislav Goroshin and Yann LeCun · 2013
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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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Implicit regularization in matrix factorization
Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nati Srebro · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Implicit regularization in deep matrix factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo · 2019
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Implicit regularization of discrete gradient dynamics in linear neural networks
Gauthier Gidel, Francis Bach, and Simon Lacoste-Julien · 2019
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
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A mathematical theory of semantic development in deep neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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Implicit bias of gradient descent on linear convolutional networks
Suriya Gunasekar, Jason D. Lee, Daniel Soudry, and Nathan Srebro · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, and Nathan Srebro · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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From variational to deterministic autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf · 2020
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, and Alexander Lerchner · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Implicit regularization in deep learning may not be explainable by norms
Noam Razin and Nadav Cohen · 2020
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