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Generalization is a central aspect of learning theory.
Optimal storage properties of neural network models
E Gardner and B Derrida · 1988
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J. Cohen · 1989
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A statistical approach to learning and generalization in layered neural networks
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Gardner-derrida neural networks with correlated patterns
W K Theumann and R Erichsen Jr · 1991
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Generalization performance of bayes optimal classification algorithm for learning a perceptron
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Statistical mechanics of learning from examples
H. S. Seung, H. Sompolinsky, and N. Tishby · 1992
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Properties of neural networks storing spatially correlated patterns
R Monasson · 1992
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Optimal storage of a neural network model: a replica symmetry-breaking solution
R Erichsen and W K Thuemann · 1993
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Storage of spatially correlated patterns in autoassociative memories
Rémi Monasson · 1993
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Weight space structure and internal representations: A direct approach to learning and generalization in multilayer neural networks
Rémi Monasson and Riccardo Zecchina · 1995
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Storage of correlated patterns in a perceptron
B Lopez, M Schroder, and M Opper · 1995
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Learning algorithm that gives the bayes generalization limit for perceptrons
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An overview of statistical learning theory
V. N. Vapnik · 1999
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Distance-based functions for image comparison
Vito Di Gesu and Valery Starovoitov · 1999
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Learning to generalize
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Statistical physics of spin glasses and information processing: an introduction
Hidetoshi Nishimori · 2001
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Origin of the computational hardness for learning with binary synapses
Haiping Huang and Yoshiyuki Kabashima · 2014
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Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses
Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, and Riccardo Zecchina · 2015
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Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
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Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes
Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, and Riccardo Zecchina · 2016
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Linear readout of object manifolds
SueYeon Chung, Daniel D. Lee, and Haim Sompolinsky · 2016
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Learning and Memory: The Brain in Action
M. A. Gluck, C. E. Myers, and E. Mercado · 2011
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Charles H Martin and Michael W Mahoney · 2017
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A high-bias, low-variance introduction to machine learning for physicists
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Classification and geometry of general perceptual manifolds
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