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Current state-of-the-art classification and detection algorithms rely on supervised training.
Signature verification using a “siamese” time delay neural network
J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. LeCun, C. Moore, E. Säckinger, and R. Shah · 1993
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Extracting slow subspaces from natural videos leads to complex cells
C. Kayser, W. Einhauser, O. Dummer, P. Konig, and K. Kding · 2001
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Slow feature analysis: Unsupervised learning of invariances
L. Wiskott and T. J. Sejnowski · 2002
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Bubbles: a unifying framework for low-level statistical properties of natural image sequences
A. Hyvärinen, J. Hurri, and J. Väyrynen · 2003
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Independent component analysis
Hyvärinen, Aapo, Karhunen, Juha, Oja, and Erkki · 2004
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Learning invariant features through topographic filter maps
K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Cited alongside, same era.
Deep learning from temporal coherence in video
H. Mobahi, R. Collobert, and J. Weston · 2009
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Learning fast approximations of sparse coding
K. Gregor and Y. LeCun · 2010
Cited alongside, same era.
Contractive auto-encoders: Explicit invariance during feature extraction
S. Rifai, P. Vincent, X. Muller, X. Galrot, and Y. Bengio · 2011
Cited alongside, same era.
Representation learning: A review and new perspectives
Y. Bengio, A. C. Courville, and P. Vincent · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Deep learning of invariant features via simulated fixations in video
W. Zou, S. Zhu, K. Yu, and A. Y. Ng · 2012
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Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
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Maxout networks
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Saturating auto-encoders
R. Goroshin and Y. LeCun · 2013
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Signal recovery from pooling representations
J. Bruna, A. Szlam, and Y. LeCun · 2014
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Learning intermediate-level representations of form and motion from natural movies
C. F. Cadieu and B. A. Olshausen · 2012
Cited alongside, same era.
Slowness and sparseness have diverging effects on complex cell learning
J.-P. Lies, R. M. Hafner, and M. Bethge · 2014
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