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A rekindled the interest in auto-encoder algorithms has been spurred by recent work on deep learning.
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M. Ranzato, C. Poultney, S. Chopra, and Y. LeCun, “Efficient learning of sparse representations with an energy-based model,” in Neural Information Processing Systems , 2006
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P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th International Conference on Machine Learning , ser. ICML ’08. New York, NY, USA: ACM, 2008, pp. 1096–1103. [Online]. Available: http://doi.acm.org/10.1145/1390156.1390294
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S. Rifai, G. Mesnil, P. Vincent, X. Muller, Y. Bengio, Y. Dauphin, and X. Glorot, “Higher order contractive auto-encoder,” in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) , 2011
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Later among the works it cites.
S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio, “Contractive auto-encoders: Explicit invariance during feature extraction,” in 28th International Conference on Machine Learning , 2011
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Later among the works it cites.
L. G. Sanchez Giraldo and J. C. Principe, “Information theoretic learning with infinitely divisible kernels,” in International Conference on Learning Representations , 2013
2013
Closest in time.
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