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6,300
Distinctive Image Features from ScaleInvariant Keypoints
, 2003
"... This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substa ..."
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Cited by 8955 (21 self)
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This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a
High dimensional graphs and variable selection with the Lasso
 ANNALS OF STATISTICS
, 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
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Cited by 736 (22 self)
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show that the proposed neighborhood selection scheme is consistent for sparse highdimensional graphs. Consistency hinges on the choice of the penalty parameter. The oracle value for optimal prediction does not lead to a consistent neighborhood estimate. Controlling instead the probability of falsely
The Infinite Hidden Markov Model
 Machine Learning
, 2002
"... We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. Th ..."
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Cited by 637 (41 self)
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We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data
Distributional Clustering Of English Words
 In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics
, 1993
"... We describe and evaluate experimentally a method for clustering words according to their dis tribution in particular syntactic contexts. Words are represented by the relative frequency distributions of contexts in which they appear, and relative entropy between those distributions is used as the si ..."
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Cited by 629 (27 self)
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as the similarity measure for clustering. Clusters are represented by average context distributions derived from the given words according to their probabilities of cluster membership. In many cases, the clusters can be thought of as encoding coarse sense distinctions. Deterministic annealing is used to find lowest
Testing for Common Trends
 Journal of the American Statistical Association
, 1988
"... Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix ..."
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Cited by 464 (7 self)
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has k unit roots and n k distinct stationary linear combinations. Our proposed tests can be viewed alternatively as tests of the number of common trends, linearly independent cointegrating vectors, or autoregressive unit roots of the vector process. Both of the proposed tests are asymptotically
A Model of Political Competition with CitizenCandidates
 JOURNAL OF ECONOMICS
, 1996
"... We develop a model of electoral competition in which citizens choose whether or not to run as candidates; a winner implements her favorite policy. The equilibrium number of candidates depends negatively on the cost of running and positively on the benefits of winning. For some parameter values all e ..."
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Cited by 409 (6 self)
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We develop a model of electoral competition in which citizens choose whether or not to run as candidates; a winner implements her favorite policy. The equilibrium number of candidates depends negatively on the cost of running and positively on the benefits of winning. For some parameter values all
Psychophysiological and Modulatory Interactions in Neuroimaging
, 1997
"... this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or taskrelated) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions b ..."
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Cited by 376 (21 self)
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this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or taskrelated) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions
The RC5 Encryption Algorithm
, 1995
"... Abstract. This document describes the RC5 encryption algorithm. RC5 is a fast symmetric block cipher suitable for hardware or software implementations. A novel feature of RC5 is the heavy use of datadependent rotations. RC5 has a variable word size, a variable number of rounds, and a variablelengt ..."
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Cited by 363 (7 self)
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word (64bit) input (plaintext) and output (ciphertext) block sizes. RC5 uses an \expanded key table, " S, derived from the user's supplied secret key. The size t of table S depends on the number r of rounds: S has t =2(r +1) words. There are thus several distinct \RC5 " algorithms
When Networks Disagree: Ensemble Methods for Hybrid Neural Networks
, 1993
"... This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, we construct a hybrid estimator which is as good or better in the MSE sense than any estimator in the population. We argu ..."
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Cited by 349 (3 self)
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in functional space which helps to avoid overfitting. 4) It utilizes local minima to construct improved estimates whereas other neural network algorithms are hindered by local minima. 5) It is ideally suited for parallel computation. 6) It leads to a very useful and natural measure of the number of distinct
SpeededUp Robust Features (SURF)
, 2008
"... This article presents a novel scale and rotationinvariant detector and descriptor, coined SURF (SpeededUp Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faste ..."
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Cited by 313 (5 self)
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This article presents a novel scale and rotationinvariant detector and descriptor, coined SURF (SpeededUp Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much
Results 1  10
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