Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDNeural Networks: Tricks of the Trade (2nd ed.) 2012
- Grégoire Montavon, Genevieve B. Orr, Klaus-Robert Müller
:
Neural Networks: Tricks of the Trade - Second Edition. Lecture Notes in Computer Science 7700, Springer 2012, ISBN 978-3-642-35288-1
Introduction
- Klaus-Robert Müller:
Introduction. 1-5
Speeding Learning
- Klaus-Robert Müller:
Speeding Learning. 7-8 - Yann LeCun, Léon Bottou, Genevieve B. Orr, Klaus-Robert Müller:
Efficient BackProp. 9-48
Regularization Techniques to Improve Generalization
- Klaus-Robert Müller:
Regularization Techniques to Improve Generalization. 49-51 - Lutz Prechelt:
Early Stopping - But When? 53-67 - Thorsteinn S. Rögnvaldsson:
A Simple Trick for Estimating the Weight Decay Parameter. 69-89 - Tony Plate:
Controlling the Hyperparameter Search in MacKay's Bayesian Neural Network Framework. 91-110 - Jan Larsen, Claus Svarer, Lars Nonboe Andersen, Lars Kai Hansen:
Adaptive Regularization in Neural Network Modeling. 111-130 - David Horn, Ury Naftaly, Nathan Intrator:
Large Ensemble Averaging. 131-137
Improving Network Models and Algorithmic Tricks
- Klaus-Robert Müller:
Improving Network Models and Algorithmic Tricks. 139-141 - Gary William Flake:
Square Unit Augmented, Radially Extended, Multilayer Perceptrons. 143-161 - Rich Caruana:
A Dozen Tricks with Multitask Learning. 163-189 - Patrick van der Smagt, Gerd Hirzinger:
Solving the Ill-Conditioning in Neural Network Learning. 191-203 - Nicol N. Schraudolph:
Centering Neural Network Gradient Factors. 205-223 - Tony Plate:
Avoiding Roundoff Error in Backpropagating Derivatives. 225-230
Representing and Incorporating Prior Knowledge in Neural Network Training
- Klaus-Robert Müller:
Representing and Incorporating Prior Knowledge in Neural Network Training. 231-233 - Patrice Y. Simard, Yann LeCun, John S. Denker, Bernard Victorri:
Transformation Invariance in Pattern Recognition - Tangent Distance and Tangent Propagation. 235-269 - Larry S. Yaeger, Brandyn J. Webb, Richard F. Lyon:
Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newton. 271-293 - Steve Lawrence, Ian Burns, Andrew D. Back, Ah Chung Tsoi, C. Lee Giles:
Neural Network Classification and Prior Class Probabilities. 295-309 - Jürgen Fritsch, Michael Finke:
Applying Divide and Conquer to Large Scale Pattern Recognition Tasks. 311-338
Tricks for Time Series
- Klaus-Robert Müller:
Tricks for Time Series. 339-341 - John Moody:
Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions. 343-367 - Ralph Neuneier, Hans-Georg Zimmermann:
How to Train Neural Networks. 369-418
Big Learning in Deep Neural Networks
- Grégoire Montavon, Klaus-Robert Müller:
Big Learning and Deep Neural Networks. 419-420 - Léon Bottou:
Stochastic Gradient Descent Tricks. 421-436 - Yoshua Bengio:
Practical Recommendations for Gradient-Based Training of Deep Architectures. 437-478 - James Martens, Ilya Sutskever:
Training Deep and Recurrent Networks with Hessian-Free Optimization. 479-535 - Ronan Collobert, Koray Kavukcuoglu, Clément Farabet:
Implementing Neural Networks Efficiently. 537-557
Better Representations: Invariant, Disentangled and Reusable
- Grégoire Montavon, Klaus-Robert Müller:
Better Representations: Invariant, Disentangled and Reusable. 559-560 - Adam Coates, Andrew Y. Ng:
Learning Feature Representations with K-Means. 561-580 - Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber:
Deep Big Multilayer Perceptrons for Digit Recognition. 581-598 - Geoffrey E. Hinton:
A Practical Guide to Training Restricted Boltzmann Machines. 599-619 - Grégoire Montavon, Klaus-Robert Müller:
Deep Boltzmann Machines and the Centering Trick. 621-637 - Jason Weston, Frédéric Ratle, Hossein Mobahi, Ronan Collobert:
Deep Learning via Semi-supervised Embedding. 639-655
Identifying Dynamical Systems for Forecasting and Control
- Grégoire Montavon, Klaus-Robert Müller:
Identifying Dynamical Systems for Forecasting and Control. 657-658 - Mantas Lukosevicius:
A Practical Guide to Applying Echo State Networks. 659-686 - Hans-Georg Zimmermann, Christoph Tietz, Ralph Grothmann:
Forecasting with Recurrent Neural Networks: 12 Tricks. 687-707 - Siegmund Duell, Steffen Udluft, Volkmar Sterzing:
Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks. 709-733 - Martin A. Riedmiller:
10 Steps and Some Tricks to Set up Neural Reinforcement Controllers. 735-757
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-04-17 20:28 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
