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Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
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Cited by 670 (10 self)
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We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing
On Random Weights and Unsupervised Feature Learning
"... Recently two anomalous results in the literature have shown that certain feature learning architectures can perform very well on object recognition tasks, without training. In this paper we pose the question, why do random weights sometimes do so well? Our answer is that certain convolutional poolin ..."
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Cited by 45 (6 self)
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Recently two anomalous results in the literature have shown that certain feature learning architectures can perform very well on object recognition tasks, without training. In this paper we pose the question, why do random weights sometimes do so well? Our answer is that certain convolutional
CONDITIONAL PRINCIPLES FOR RANDOM WEIGHTED MEASURES
, 2005
"... In this paper, we prove a conditional principle of Gibbs type for random weighted measures of the form Ln = ..."
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Cited by 3 (2 self)
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In this paper, we prove a conditional principle of Gibbs type for random weighted measures of the form Ln =
Randomly Weighted Series of Contractions in Hilbert Spaces
"... Conditions are given for the convergence of randomly weighted series of contractions ..."
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Cited by 3 (3 self)
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Conditions are given for the convergence of randomly weighted series of contractions
SemiSupervised Learning Using Gaussian Fields and Harmonic Functions
 IN ICML
, 2003
"... An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
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Cited by 752 (14 self)
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An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning
INFLUENCE OF RANDOM WEIGHT DEVIATIONS
"... Abstract: This article is the second part of a work dealing with the optoelectronic implementation of artificial neural networks. The authors analyze the problems involved by using computergenerated holograms (CGH) for these interconnections and some methods of designing such diffractive elements. ..."
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. The authors also analyze the error sources and the consequences caused by random deviations of the neurons interconnection weights from the accurately computed values. The theoretical considerations are illustrated by designing an auto associative memory built for graphic pattern recognition. Neurons
Constant compression and random weights
, 2012
"... Omega numbers, as considered in algorithmic randomness, are by definition real numbers that are equal to the halting probability of a universal prefixfree Turing machine. Omega numbers are obviously leftr.e., i.e., are effectively approximable from below. Furthermore, among all leftr.e. real numb ..."
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Cited by 1 (1 self)
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Omega numbers, as considered in algorithmic randomness, are by definition real numbers that are equal to the halting probability of a universal prefixfree Turing machine. Omega numbers are obviously leftr.e., i.e., are effectively approximable from below. Furthermore, among all leftr.e. real
Empirical exchange rate models of the Seventies: do they fit out of sample?
 JOURNAL OF INTERNATIONAL ECONOMICS
, 1983
"... This study compares the outofsample forecasting accuracy of various structural and time series exchange rate models. We find that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar/yen and tradeweighted dollar exch ..."
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Cited by 854 (12 self)
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This study compares the outofsample forecasting accuracy of various structural and time series exchange rate models. We find that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar/yen and tradeweighted dollar
Image denoising using a scale mixture of Gaussians in the wavelet domain
 IEEE TRANS IMAGE PROCESSING
, 2003
"... We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vecto ..."
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Cited by 513 (17 self)
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We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian
On the replica symmetry for random weighted matchings
, 1991
"... Abstract. We present an extensive simulation on random weighted matchings dealing with the possible existence of the breaking of the replica symmetry. Using the socalled postopimal analysis of combinatorial optimization we are able to draw the definite conclusion on the replica behaviour for this c ..."
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Abstract. We present an extensive simulation on random weighted matchings dealing with the possible existence of the breaking of the replica symmetry. Using the socalled postopimal analysis of combinatorial optimization we are able to draw the definite conclusion on the replica behaviour
Results 1  10
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712,180