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Self-delimiting (SLIM) programs are a central concept of theoretical computer science, particularly algorithmic information & probability theory, and asymptotically optimal program search (AOPS).
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Long short-term memory
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An Introduction to Kolmogorov Complexity and its Applications (2nd edition)
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Discovering neural nets with low Kolmogorov complexity and high generalization capability
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Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement
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Gradient-based learning applied to document recognition
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Processing images by semi-linear predictability minimization
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Training recurrent networks by EVOLINO
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Efficient non-linear control through neuroevolution
F. J. Gomez, J. Schmidhuber, and R. Miikkulainen · 2008
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Unconstrained on-line handwriting recognition with recurrent neural networks
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Natural evolution strategies
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A novel connectionist system for improved unconstrained handwriting recognition
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Offline handwriting recognition with multidimensional recurrent neural networks
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LSTM recurrent networks learn simple context free and context sensitive languages
F. A. Gers and J. Schmidhuber · 2001
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Completely derandomized self-adaptation in evolution strategies
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Gradient flow in recurrent nets: the difficulty of learning long-term dependencies
S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber · 2001
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Learning precise timing with LSTM recurrent networks
F. A. Gers, N. Schraudolph, and J. Schmidhuber · 2002
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Spiking Neuron Models
W. Gerstner and W. K. Kistler · 2002
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Neural circuits for pattern recognition with small total wire length
R. A. Legenstein and W. Maass · 2002
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The Intelligent Movement Machine: An Ethological Perspective on the Primate Motor System
M. Graziano · 2009
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Learning multiple layers of features from tiny images
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Efficient natural evolution strategies
Yi Sun, D. Wierstra, T. Schaul, and J. Schmidhuber · 2009
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Stochastic search using the natural gradient
Yi Sun, D. Wierstra, T. Schaul, and J. Schmidhuber · 2009
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Deep big simple neural nets for handwritten digit recogntion
D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber · 2010
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Exponential Natural Evolution Strategies
T. Glasmachers, T. Schaul, Y. Sun, D. Wierstra, and J. Schmidhuber · 2010
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Evolving neural networks in compressed weight space
J. Koutnik, F. Gomez, and J. Schmidhuber · 2010
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Searching for minimal neural networks in Fourier space
J. Koutník, F. Gomez, and J. Schmidhuber · 2010
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Evaluation of pooling operations in convolutional architectures for object recognition
D. Scherer, A. Müller, and S. Behnke · 2010
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Recurrent policy gradients
D. Wierstra, A. Foerster, J. Peters, and J. Schmidhuber · 2010
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Convolutional neural network committees for handwritten character classification
D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber · 2011
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Flexible, high performance convolutional neural networks for image classification
D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber · 2011
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A committee of neural networks for traffic sign classification
D. C. Ciresan, U. Meier, J. Masci, and J. Schmidhuber · 2011
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The two-dimensional organization of behavior
M. Ring, T. Schaul, and J. Schmidhuber · 2011
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On fast deep nets for AGI vision
J. Schmidhuber, D. Ciresan, U. Meier, J. Masci, and A. Graves · 2011
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Generating text with recurrent neural networks
I. Sutskever, J. Martens, and G. Hinton · 2011
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Deep neural networks segment neuronal membranes in electron microscopy images
D. C. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber · 2012
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Multi-column deep neural network for traffic sign classification
D. C. Ciresan, U. Meier, J. Masci, and J. Schmidhuber · 2012
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Multi-column deep neural networks for image classification
D. C. Ciresan, U. Meier, and J. Schmidhuber · 2012
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Compressed networks complexity search
F. J. Gomez, J. Koutník, and J. Schmidhuber · 2012
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