Efficient BackProp
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Klaus-Robert Müller | Yann LeCun | Léon Bottou | Genevieve B. Orr | L. Bottou | Yann LeCun | G. Orr | K. Müller
[1] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[2] Shun-ichi Amari,et al. Complexity Issues in Natural Gradient Descent Method for Training Multilayer Perceptrons , 1998, Neural Computation.
[3] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[4] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[5] Shun-ichi Amari,et al. The Efficiency and the Robustness of Natural Gradient Descent Learning Rule , 1997, NIPS.
[6] Genevieve B. Orr,et al. Removing Noise in On-Line Search using Adaptive Batch Sizes , 1996, NIPS.
[7] Andreas Ziehe,et al. Adaptive On-line Learning in Changing Environments , 1996, NIPS.
[8] Shun-ichi Amari,et al. Neural Learning in Structured Parameter Spaces - Natural Riemannian Gradient , 1996, NIPS.
[9] Saad,et al. Exact solution for on-line learning in multilayer neural networks. , 1995, Physical review letters.
[10] Mark J. L. Orr,et al. Regularization in the Selection of Radial Basis Function Centers , 1995, Neural Computation.
[11] Haim Sompolinsky,et al. On-line Learning of Dichotomies: Algorithms and Learning Curves. , 1995, NIPS 1995.
[12] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[13] W. Wiegerinck,et al. Stochastic dynamics of learning with momentum in neural networks , 1994 .
[14] Wray L. Buntine,et al. Computing second derivatives in feed-forward networks: a review , 1994, IEEE Trans. Neural Networks.
[15] Barak A. Pearlmutter. Fast Exact Multiplication by the Hessian , 1994, Neural Computation.
[16] Patrick van der Smagt. Minimisation methods for training feedforward neural networks , 1994, Neural Networks.
[17] J. G. Taylor,et al. Mathematical Approaches to Neural Networks , 1993 .
[18] Martin Fodslette Møller,et al. A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.
[19] Hilbert J. Kappen,et al. On-line learning processes in artificial neural networks , 1993 .
[20] Barak A. Pearlmutter,et al. Automatic Learning Rate Maximization by On-Line Estimation of the Hessian's Eigenvectors , 1992, NIPS 1992.
[21] M. Moller,et al. Supervised learning on large redundant training sets , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.
[22] Richard S. Sutton,et al. Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta , 1992, AAAI.
[23] Roberto Battiti,et al. First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.
[24] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[25] Pierre Priouret,et al. Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.
[26] John E. Moody,et al. Note on Learning Rate Schedules for Stochastic Optimization , 1990, NIPS.
[27] Yann LeCun,et al. Second Order Properties of Error Surfaces , 1990, NIPS.
[28] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.
[29] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[30] Yann LeCun,et al. Generalization and network design strategies , 1989 .
[31] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[32] R. Fletcher. Practical Methods of Optimization , 1988 .
[33] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[34] Alberto L. Sangiovanni-Vincentelli,et al. Efficient Parallel Learning Algorithms for Neural Networks , 1988, NIPS.
[35] Yann LeCun. PhD thesis: Modeles connexionnistes de l'apprentissage (connectionist learning models) , 1987 .
[36] G. Golub. Matrix computations , 1983 .