Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition Marc’Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, Yann LeCun Courant Institute of Mathematical Sciences, New York Universi

First Page		Document Content
Date: 2009-08-08 18:43:08 Neural networks Learning Statistical classification Pattern recognition Supervised learning Autoencoder Unsupervised learning Caltech 101 Support vector machine Machine learning Statistics Artificial intelligence		Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition Marc’Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, Yann LeCun Courant Institute of Mathematical Sciences, New York Universi Add to Reading List Source URL: www.cs.toronto.edu Download Document from Source Website File Size: 337,22 KB Share Document on Facebook

	Syntax-Directed Variational Autoencoder for Molecule Generation Hanjun Dai1* , Yingtao Tian2* , Bo Dai1 , Steven Skiena2 , Le Song1 1 College of Computing, Georgia Institute of Technology 2 DocID: 1vlR1 - View Document
	Semi-Supervised Recursive Autoencoder Si Chen and Yufei Wang Department of Electrical and Computer Engineering University of California San Diego {sic046, yuw176}@ucsd.edu DocID: 1v727 - View Document
	An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P1 ∗ , Stanislas Lauly2 ∗ , Hugo Larochelle2 , Mitesh M Khapra3 , Balaraman Ravindran1 , Vikas Raykar3 , Amrita Saha3 1 DocID: 1v23p - View Document
	On Nonparametric Guidance for Learning Autoencoder Representations Jasper Snoek University of Toronto DocID: 1uwmD - View Document
	arXiv:1610.00291v1 [cs.CV] 2 OctDeep Feature Consistent Variational Autoencoder Xianxu Hou University of Nottingham, Ningbo China DocID: 1tDNC - View Document