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Understanding the inductive bias of neural networks is critical to explaining their ability to generalise.
The perceptron: a probabilistic model for information storage and organization in the brain
Frank Rosenblatt · 1958
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On the complexity of finite sequences
Abraham Lempel and Jacob Ziv · 1976
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Generalization and parameter estimation in feedforward nets: Some experiments
Nelson Morgan and Hervé Bourlard · 1990
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A simple weight decay can improve generalization
Anders Krogh and John A Hertz · 1992
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Keeping neural networks simple by minimizing the description length of the weights
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Discovering neural nets with low kolmogorov complexity and high generalization capability
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Some pac-bayesian theorems
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Discrete mathematics of neural networks: selected topics , volume 8
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An introduction to Kolmogorov complexity and its applications , volume 3
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Dropout: A simple way to prevent neural networks from overfitting
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Gaussian process behaviour in wide deep neural networks
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Bayesian convolutional neural networks with many channels are gaussian processes
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