Deep, Big, Simple Neural Nets for Handwritten Digit Recognition
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Luca Maria Gambardella | Jürgen Schmidhuber | Ueli Meier | Dan C. Ciresan | J. Schmidhuber | D. Ciresan | L. Gambardella | U. Meier
[1] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[2] Yann LeCun,et al. Une procedure d'apprentissage pour reseau a seuil asymmetrique (A learning scheme for asymmetric threshold networks) , 1985 .
[3] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[4] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[5] Sepp Hochreiter,et al. Untersuchungen zu dynamischen neuronalen Netzen , 1991 .
[6] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[7] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[8] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[9] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[10] Patrice Y. Simard,et al. Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..
[11] Peter Norvig,et al. Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.
[12] Bernhard Schölkopf,et al. Training Invariant Support Vector Machines , 2002, Machine Learning.
[13] Philipp Slusallek,et al. Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.
[14] Patrice Y. Simard,et al. Using GPUs for machine learning algorithms , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).
[15] Patrice Y. Simard,et al. High Performance Convolutional Neural Networks for Document Processing , 2006 .
[16] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[17] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[18] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[19] Geoffrey E. Hinton,et al. To recognize shapes, first learn to generate images. , 2007, Progress in brain research.
[20] John F. Kalaska,et al. Computational neuroscience : theoretical insights into brain function , 2007 .
[21] Ching Y. Suen,et al. A trainable feature extractor for handwritten digit recognition , 2007, Pattern Recognit..
[22] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.
[23] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[24] Geoffrey E. Hinton,et al. Implicit Mixtures of Restricted Boltzmann Machines , 2008, NIPS.
[25] Geoffrey E. Hinton,et al. Deep Belief Networks for phone recognition , 2009 .
[26] Sven Behnke,et al. Accelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors , 2010, ICANN.