Convex Neural Networks
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Nicolas Le Roux | Pascal Vincent | Yoshua Bengio | Patrice Marcotte | Olivier Delalleau | Yoshua Bengio | Olivier Delalleau | Pascal Vincent | P. Marcotte
[1] J. Friedman,et al. Projection Pursuit Regression , 1981 .
[2] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[3] Geoffrey E. Hinton,et al. Learning representations by back-propagation errors, nature , 1986 .
[4] J. Nadal,et al. Learning in feedforward layered networks: the tiling algorithm , 1989 .
[5] Stephen I. Gallant,et al. Perceptron-based learning algorithms , 1990, IEEE Trans. Neural Networks.
[6] Patrice Marcotte,et al. Novel approaches to the discrimination problem , 1992, ZOR Methods Model. Oper. Res..
[7] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[8] Kenneth O. Kortanek,et al. Semi-Infinite Programming: Theory, Methods, and Applications , 1993, SIAM Rev..
[9] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[10] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[11] Marco Muselli,et al. On convergence properties of pocket algorithm , 1997, IEEE Trans. Neural Networks.
[12] Yves Grandvalet. Least Absolute Shrinkage is Equivalent to Quadratic Penalization , 1998 .
[13] Peter L. Bartlett,et al. Boosting Algorithms as Gradient Descent , 1999, NIPS.
[14] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[15] Ronald,et al. Learning representations by backpropagating errors , 2004 .
[16] Gunnar Rätsch,et al. Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces , 2002, Machine Learning.
[17] Nicolas Le Roux,et al. Efficient Non-Parametric Function Induction in Semi-Supervised Learning , 2004, AISTATS.
[18] Yuval Rabani,et al. Linear Programming , 2007, Handbook of Approximation Algorithms and Metaheuristics.