• Publications
  • Influence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
  • S. Geman, D. Geman
  • Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 November 1984
TLDR
We make an analogy between images and statistical mechanics systems. Expand
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Neural Networks and the Bias/Variance Dilemma
TLDR
We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. Expand
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Statistical methods for tomographic image reconstruction
  • 603
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Boundary Detection by Constrained Optimization
TLDR
A statistical framework is used for finding boundaries and for partitioning scenes into homogeneous regions. Expand
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A Limit Theorem for the Norm of Random Matrices
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Estimators for Stochastic "Unification-Based" Grammars
TLDR
Log-linear models provide a statistically sound framework for Stochastic "Unification-Based" Grammars (SUBGs) and stochastic versions of other kinds of grammars. Expand
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Nonparametric Maximum Likelihood Estimation by the Method of Sieves
Maximum likelihood estimation often fails when the parameter takes values in an infinite dimensional space. For example, the maximum likelihood method cannot be applied to the completelyExpand
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Diffusions for global optimizations
We seek a global minimum of $U:[0,1]^n \to R$. The solution to $({d / {dt}})x_t = - \nabla U(x_t )$ will find local minima. The solution to $dx_t = - \nabla U(x_t )dt + \sqrt {2T} dw_t $, where w isExpand
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Estimation of Probabilistic Context-Free Grammars
TLDR
The assignment of probabilities to the productions of a context-free grammar may generate improper distribution: the probability of all finite parse trees is less than one. Expand
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