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A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data.
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Xiaojin Zhu · 2011
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Adam Coates and Andrew Y Ng · 2012
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Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Building high-level features using large scale unsupervised learning
Quoc V Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Gregory S. Corrado, Kai Chen, Jeffrey Dean, and Andrew Y Ng · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Learning to disentangle factors of variation with manifold interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 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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Discovering hidden factors of variation in deep networks
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Andrew M Dai and Quoc V Le · 2015
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Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
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Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Meta networks
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Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter J Liu, and Quoc V Le · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Loss is its own reward: Self-supervision for reinforcement learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, and Trevor Darrell · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S Zemel · 2017
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Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Identity mappings in deep residual networks
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Data-dependent initializations of convolutional neural networks
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Disentangling factors of variation in deep representation using adversarial training
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A Efros · 2017
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Understanding and improving interpolation in autoencoders via an adversarial regularizer
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Realistic evaluation of deep semi-supervised learning algorithms
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