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Neural Networks and Spin Glasses
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Generalization of finite size Boolean perceptrons with genetic algorithms
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Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
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Statistical Physics of Spin Glasses and Information Processing
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Mean-Field Theory of Phase Transitions
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Replica Symmetry Breaking
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Gauge Theory of Spin Glasses
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Error-Correcting Codes
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Image Restoration
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Optimization Problems
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First-order transition to perfect generalization in a neural network with binary synapses
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Calculation of learning curves for inconsistent algorithms
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Learning by examples from a nonuniform distribution
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Inference and chaos by a network of nonmonotonic neurons
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Finite size scaling of the Bayesian perceptron
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Multifractala prioriprobability distribution for the perceptron
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Receiver operating characteristics of perceptrons: Influence of sample size and prevalence
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Statistical mechanics of learning with soft margin classifiers
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Generalization performance of complex adaptive tasks
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Generalization error in a self-similar committee machine
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Mean Field Approach to Bayes Learning in Feed-Forward Neural Networks
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Unsupervised Learning by Examples: On-line versus Off-line
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The statistical mechanics of learning a rule
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Machine learning and the physical sciences
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Generalization in a Large Committee Machine
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Error vs. Rejection Curve for the Perceptron
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Learning with a Temperature-Dependent Algorithm
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Analysing Cluster Formation by Replica Method
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Noise robustness in multilayer neural networks
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On the center of mass of Ising vectors
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