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Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics.
Connectionist learning procedures
Hinton, Geoffrey E · 1989
Earlier work this paper cites.
A training algorithm for optimal margin classifiers
Boser, Bernhard E., Guyon, Isabelle M., and Vapnik, Vladimir N · 1992
Earlier work this paper cites.
The nature of statistical learning theory
Vapnik, V. N · 1995
Earlier work this paper cites.
Decision boundary focused neural network classifier
Zhong, Shi and Ghosh, Joydeep · 2000
Earlier work this paper cites.
Ssvm: A smooth support vector machine for classification
Lee, Yuh-Jye and Mangasarian, O. L · 2001
Earlier work this paper cites.
A comparison of methods for multiclass support vector machines
Hsu, Chih-Wei and Lin, Chih-Jen · 2002
Earlier work this paper cites.
A gentle hessian for efficient gradient descent
Collobert, R. and Bengio, S · 2004
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Hadsell, Raia, Chopra, Sumit, and Lecun, Yann · 2006
Earlier work this paper cites.
Large-scale learning with SVM and convolutional for generic object categorization
Huang, F. J. and LeCun, Y · 2006
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Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y · 2009
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Mohamed, A., Dahl, G. E., and Hinton, G. E · 2009
Tiled convolutional neural networks
Quoc, L., Ngiam, J., Chen, Z., Chia, D., Koh, P. W., and Ng, A · 2010
Later among the works it cites.
High-performance neural networks for visual object classification
Ciresan, D., Meier, U., Masci, J., Gambardella, L. M., and Schmidhuber, J · 2011
Later among the works it cites.
An analysis of single-layer networks in unsupervised feature learning
Coates, Adam, Ng, Andrew Y., and Lee, Honglak · 2011
Later among the works it cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Later among the works it cites.
Convolutional Neural Support Vector Machines: Hybrid visual pattern classifiers for multi-robot systems
Nagi, J., Di Caro, G. A., Giusti, A., , Nagi, F., and Gambardella, L · 2012
Later among the works it cites.
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Phone recognition with the mean-covariance restricted Boltzmann machine
Dahl, G. E., Ranzato, M., Mohamed, A., and Hinton, G. E · 2010
Cited alongside, same era.
The manifold tangent classifier
Rifai, Salah, Dauphin, Yann, Vincent, Pascal, Bengio, Yoshua, and Muller, Xavier
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Adding noise to the input of a model trained with a regularized objective
Rifai, Salah, Glorot, Xavier, Bengio, Yoshua, and Vincent, Pascal
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Deep learning made easier by linear transformations in perceptrons
Raiko, Tapani, Valpola, Harri, and LeCun, Yann · 2012
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Learning with Recursive Perceptual Representations
Vinyals, O., Jia, Y., Deng, L., and Darrell, T · 2012
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