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Despite the popularity of deep learning, structure learning for deep models remains a relatively under-explored area.
Approximating discrete probability distributions with dependence trees
C Chow and Cong Liu · 1968
Earlier work this paper cites.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
Estimating the dimension of a model
Gideon Schwarz et al · 1978
Earlier work this paper cites.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl · 1988
Earlier work this paper cites.
Dynamic node creation in backpropagation networks
Timur Ash · 1989
Earlier work this paper cites.
Enhanced training algorithms, and integrated training/architecture selection for multilayer perceptron networks
Martin G Bello · 1992
Earlier work this paper cites.
Constructive algorithms for structure learning in feedforward neural networks for regression problems
Tin-Yau Kwok and Dit-Yan Yeung · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Hierarchical latent class models for cluster analysis
Nevin L Zhang · 2004
Earlier work this paper cites.
Learning the structure of deep sparse graphical models
Ryan Prescott Adams, Hanna M Wallach, and Zoubin Ghahramani · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
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Progressive em for latent tree models and hierarchical topic detection
Peixian Chen, Nevin L Zhang, Leonard KM Poon, and Zhourong Chen · 2016
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Deeptox: toxicity prediction using deep learning
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, and Sepp Hochreiter · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J Smola, and Eduard H Hovy · 2016
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Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
Later among the works it cites.
Latent tree models for hierarchical topic detection
Peixian Chen, Nevin L Zhang, Tengfei Liu, Leonard KM Poon, Zhourong Chen, and Farhan Khawar · 2017
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Sparse boltzmann machines with structure learning as applied to text analysis
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Self-normalizing neural networks
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Pruning filters for efficient convnets
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Large-scale evolution of image classifiers
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