Neural networks : tricks of the trade / Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller (eds.)
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| Online Access: |
Full Text (via Springer) |
|---|---|
| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Berlin ; New York :
Springer,
2012.
|
| Edition: | 2nd ed. |
| Series: | Lecture notes in computer science ;
7700. LNCS sublibrary. Theoretical computer science and general issues. |
| Subjects: |
Table of Contents:
- Introduction / Klaus-Robert Müller
- Speeding Learning / Klaus-Robert Müller
- Efficient BackProp / Yann A. LeCun, Léon Bottou, Genevieve B. Orr and Klaus-Robert Müller
- Regularization Techniques to Improve Generalization / Klaus-Robert Müller
- Early Stopping
- But When? / Lutz Prechelt
- A Simple Trick for Estimating the Weight Decay Parameter / Thorsteinn S. Rögnvaldsson
- Controlling the Hyperparameter Search in MacKay's Bayesian Neural Network Framework / Tony Plate
- Adaptive Regularization in Neural Network Modeling / Jan Larsen, Claus Svarer, Lars Nonboe Andersen and Lars Kai Hansen
- Large Ensemble Averaging / David Horn, Ury Naftaly and Nathan Intrator
- Improving Network Models and Algorithmic Tricks / Klaus-Robert Müller
- Square Unit Augmented, Radially Extended, Multilayer Perceptrons / Gary William Flake
- A Dozen Tricks with Multitask Learning / Rich Caruana
- Solving the Ill-Conditioning in Neural Network Learning / Patrick van der Smagt and Gerd Hirzinger
- Centering Neural Network Gradient Factors / Nicol N. Schraudolph
- Avoiding Roundoff Error in Backpropagating Derivatives / Tony Plate.
- Representing and Incorporating Prior Knowledge in Neural Network Training / Klaus-Robert Müller
- Transformation Invariance in Pattern Recognition
- Tangent Distance and Tangent Propagation / Patrice Y. Simard, Yann A. LeCun, John S. Denker and Bernard Victorri
- Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newton / Larry S. Yaeger, Brandyn J. Webb and Richard F. Lyon
- Neural Network Classification and Prior Class Probabilities / Steve Lawrence, Ian Burns, Andrew Back, Ah Chung Tsoi and C. Lee Giles
- Applying Divide and Conquer to Large Scale Pattern Recognition Tasks / Jürgen Fritsch and Michael Finke
- Tricks for Time Series / Klaus-Robert Müller
- Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions / John Moody
- How to Train Neural Networks / Ralph Neuneier and Hans Georg Zimmermann
- Big Learning and Deep Neural Networks / Grégoire Montavon and Klaus-Robert Müller
- Stochastic Gradient Descent Tricks / Léon Bottou
- Practical Recommendations for Gradient-Based Training of Deep Architectures / Yoshua Bengio
- Training Deep and Recurrent Networks with Hessian-Free Optimization / James Martens and Ilya Sutskever.
- Implementing Neural Networks Efficiently / Ronan Collobert, Koray Kavukcuoglu and Clément Farabet
- Better Representations: Invariant, Disentangled and Reusable / Grégoire Montavon and Klaus-Robert Müller
- Learning Feature Representations with K-Means / Adam Coates and Andrew Y. Ng
- Deep Big Multilayer Perceptrons for Digit Recognition / Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella and Jürgen Schmidhuber
- A Practical Guide to Training Restricted Boltzmann Machines / Geoffrey E. Hinton
- Deep Boltzmann Machines and the Centering Trick / Grégoire Montavon and Klaus-Robert Müller
- Deep Learning via Semi-supervised Embedding / Jason Weston, Frédéric Ratle, Hossein Mobahi and Ronan Collobert
- Identifying Dynamical Systems for Forecasting and Control / Grégoire Montavon and Klaus-Robert Müller
- A Practical Guide to Applying Echo State Networks / Mantas Lukoševičius
- Forecasting with Recurrent Neural Networks: 12 Tricks / Hans-Georg Zimmermann, Christoph Tietz and Ralph Grothmann
- Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks / Siegmund Duell, Steffen Udluft and Volkmar Sterzing
- 10 Steps and Some Tricks to Set up Neural Reinforcement Controllers / Martin Riedmiller.