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We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model distribution.
Spline models for observational data
Wahba, Grace · 1990
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Fast exact multiplication by the hessian
Pearlmutter, Barak A · 1994
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Information theory and an extension of the maximum likelihood principle
Akaike, Hirotugu · 1998
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Eigenvalue computation in the 20th century
Golub, Gene H and Van der Vorst, Henk A · 2000
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Vicinal risk minimization
Chapelle, Olivier, Weston, Jason, Bottou, Léon, and Vapnik, Vladimir · 2001
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The elements of statistical learning
Friedman, Jerome, Hastie, Trevor, and Tibshirani, Robert · 2001
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Learning deep architectures for ai
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What is the best multi-stage architecture for object recognition?
Jarrett, Kevin, Kavukcuoglu, Koray, Ranzato, Marc’Aurelio, and LeCun, Yann · 2009
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Algebraic geometry and statistical learning theory
Watanabe, Sumio · 2009
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Theano: a CPU and GPU math expression compiler
Bergstra, James, Breuleux, Olivier, Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Desjardins, Guillaume, Turian, Joseph, Warde-Farley, David, and Bengio, Yoshua · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, Vinod and Hinton, Geoffrey E · 2010
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An analysis of single-layer networks in unsupervised feature learning
Coates, Adam, Ng, Andrew Y, and Lee, Honglak · 2011
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Deep sparse rectifier neural networks
Glorot, Xavier, Bordes, Antoine, and Bengio, Yoshua · 2011
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The manifold tangent classifier
Rifai, Salah, Dauphin, Yann N, Vincent, Pascal, Bengio, Yoshua, and Muller, Xavier · 2011
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Theano: new features and speed improvements
Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Bergstra, James, Goodfellow, Ian J., Bergeron, Arnaud, Bouchard, Nicolas, and Bengio, Yoshua · 2012
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Deep learning via semi-supervised embedding
Weston, Jason, Ratle, Frédéric, Mobahi, Hossein, and Collobert, Ronan · 2012
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Semi-supervised learning with deep generative models
Kingma, Diederik, Mohamed, Shakir, Rezende, Danilo Jimenez, and Welling, Max · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
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Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2015
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Towards deep neural network architectures robust to adversarial examples
Gu, Shixiang and Rigazio, Luca · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2015
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Maxout networks
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Learning with pseudo-ensembles
Bachman, Phil, Alsharif, Ouais, and Precup, Doina · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014
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Deep learning
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Semi-supervised learning with ladder network
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