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Autoencoders are among the earliest introduced nonlinear models for unsupervised learning.
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski, A learning algorithm for boltzmann machines , Cognitive science 9
1985
Earlier work this paper cites.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams, Learning internal representations by error propagation , Tech. report, California Univ San Diego La Jolla Inst for Cognitive Science, 1985
1985
Earlier work this paper cites.
David E Rumelhart and David Zipser, Feature discovery by competitive learning , Cognitive science 9
1985
Earlier work this paper cites.
Hervé Bourlard and Yves Kamp, Auto-association by multilayer perceptrons and singular value decomposition , Biological cybernetics 59
1988
Earlier work this paper cites.
Pierre Baldi and Kurt Hornik, Neural networks and principal component analysis: Learning from examples without local minima , Neural networks 2
1989
Earlier work this paper cites.
Jonathan Goodman, Thomas Y Hou, and John Lowengrub, Convergence of the point vortex method for the 2-d euler equations , Communications on Pure and Applied Mathematics 43
1990
Earlier work this paper cites.
Alain-Sol Sznitman, Topics in propagation of chaos , Ecole d’été de probabilités de Saint-Flour XIX—1989, Springer, 1991, pp. 165–251
1991
Earlier work this paper cites.
Sever Silvestru Dragomir, Some gronwall type inequalities and applications , Nova Science Publishers New York, 2003
2003
Earlier work this paper cites.
Andrew Y Ng, Feature selection, l 1 vs. l 2 regularization, and rotational invariance , Proceedings of the twenty-first international conference on Machine learning, 2004, p. 78
2004
Earlier work this paper cites.
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh, A fast learning algorithm for deep belief nets , Neural computation 18
2006
Earlier work this paper cites.
Geoffrey E Hinton and Ruslan R Salakhutdinov, Reducing the dimensionality of data with neural networks , science 313
2006
Earlier work this paper cites.
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle, Greedy layer-wise training of deep networks , Advances in neural information processing systems, 2007, pp. 153–160
2007
Earlier work this paper cites.
Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, and Yann L Cun, Efficient learning of sparse representations with an energy-based model , Advances in neural information processing systems, 2007, pp. 1137–1144
2007
Earlier work this paper cites.
Nicolas Le Roux and Yoshua Bengio, Representational power of restricted boltzmann machines and deep belief networks , Neural computation 20
2008
Earlier work this paper cites.
2010
Earlier work this paper cites.
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion , Journal of machine learning research 11
2010
Earlier work this paper cites.
Guido Montufar and Nihat Ay, Refinements of universal approximation results for deep belief networks and restricted boltzmann machines , Neural computation 23
2011
Earlier work this paper cites.
Quoc Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg Corrado, Jeff Dean, and Andrew Ng, Building high-level features using large scale unsupervised learning , Proceedings of the 29th International Conference on Machine Learning, 2012
2012
Earlier work this paper cites.
Michel Ledoux and Michel Talagrand, Probability in banach spaces: isoperimetry and processes , Springer Science & Business Media, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
Guillaume Alain and Yoshua Bengio, What regularized auto-encoders learn from the data-generating distribution , The Journal of Machine Learning Research 15
2014
Earlier work this paper cites.
2015
Cited alongside, same era.
Ali Mousavi, Ankit B Patel, and Richard G Baraniuk, A deep learning approach to structured signal recovery , 2015 53rd annual allerton conference on communication, control, and computing (Allerton), IEEE, 2015, pp. 1336–1343
2015
Cited alongside, same era.
2017
Cited alongside, same era.
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik, Emnist: Extending mnist to handwritten letters , 2017 International Joint Conference on Neural Networks (IJCNN), IEEE, 2017, pp. 2921–2926
2017
Cited alongside, same era.
2019
Later among the works it cites.
2019
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Ping Li and Phan-Minh Nguyen, On random deep weight-tied autoencoders: Exact asymptotic analysis, phase transitions, and implications to training , International Conference on Learning Representations, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
Lénaïc Chizat and Francis Bach, On the global convergence of gradient descent for over-parameterized models using optimal transport , Advances in Neural Information Processing Systems, 2018, pp. 3040–3050
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Song Mei, Andrea Montanari, and Phan-Minh Nguyen, A mean field view of the landscape of two-layers neural networks , Proceedings of the National Academy of Sciences, vol. 115, 2018, pp. 7665–7671
2018
Cited alongside, same era.
Samuel Ocko, Jack Lindsey, Surya Ganguli, and Stephane Deny, The emergence of multiple retinal cell types through efficient coding of natural movies , Advances in Neural Information Processing Systems, 2018, pp. 9389–9400
2018
Cited alongside, same era.
Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu, Ashish Arora, Tejaswini Ganapathi, Sang Chin, and Trac D Tran, Sparse coding and autoencoders , 2018 IEEE International Symposium on Information Theory (ISIT), IEEE, 2018, pp. 36–40
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
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2019
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2019
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2019
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Colin Wei, Jason D Lee, Qiang Liu, and Tengyu Ma, Regularization matters: Generalization and optimization of neural nets vs their induced kernel , Advances in Neural Information Processing Systems, 2019, pp. 9709–9721
2019
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2019
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2020
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2020
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2020
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2020
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2020
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2020
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2020
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2020
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Adityanarayanan Radhakrishnan, Mikhail Belkin, and Caroline Uhler, Overparameterized neural networks implement associative memory , Proceedings of the National Academy of Sciences (2020)
2020
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