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10,727
Learning to rank using gradient descent
 In ICML
, 2005
"... We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data f ..."
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Cited by 534 (17 self)
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We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data
Dualtask interference in simple tasks: Data and theory
 Psychological Bulletin
, 1994
"... People often have trouble performing 2 relatively simple tasks concurrently. The causes of this interference and its implications for the nature of attentional limitations have been controversial for 40 years, but recent experimental findings are beginning to provide some answers. Studies of the psy ..."
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Cited by 434 (12 self)
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People often have trouble performing 2 relatively simple tasks concurrently. The causes of this interference and its implications for the nature of attentional limitations have been controversial for 40 years, but recent experimental findings are beginning to provide some answers. Studies
SELF: The power of simplicity
, 1991
"... SELF is an objectoriented language for exploratory programming based on a small number of simple and concrete ideas: prototypes, slots, and behavior. Prototypes combine inheritance and instantiation to provide a framework that is simpler and more flexible than most objectoriented languages. Slots ..."
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Cited by 640 (19 self)
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SELF is an objectoriented language for exploratory programming based on a small number of simple and concrete ideas: prototypes, slots, and behavior. Prototypes combine inheritance and instantiation to provide a framework that is simpler and more flexible than most objectoriented languages
Unsupervised Models for Named Entity Classification
 In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
, 1999
"... This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabe ..."
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Cited by 542 (4 self)
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of unlabeled data can reduce the requirements for supervision to just 7 simple “seed ” rules. The approach gains leverage from natural redundancy in the data: for many namedentity instances both the spelling of the name and the context in which it appears are sufficient to determine its type. We present two
Online Learning with Kernels
, 2003
"... Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the socalled kernel trick with the large margin idea. There has been little u ..."
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Cited by 2831 (123 self)
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Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the socalled kernel trick with the large margin idea. There has been little
The irreducibility of the space of curves of given genus
 Publ. Math. IHES
, 1969
"... Fix an algebraically closed field k. Let Mg be the moduli space of curves of genus g over k. The main result of this note is that Mg is irreducible for every k. Of course, whether or not M s is irreducible depends only on the characteristic of k. When the characteristic s o, we can assume that k ~ ..."
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Cited by 506 (2 self)
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~ (1, and then the result is classical. A simple proof appears in EnriquesChisini [E, vol. 3, chap. 3], based on analyzing the totality of coverings of p1 of degree n, with a fixed number d of ordinary branch points. This method has been extended to char. p by William Fulton [F], using specializations
Stability of nonautonomous difference equations: simple ideas leading to useful results
, 2009
"... We address the stability properties in a nonautonomous difference equation xnþ1 f ðn; xn;...; xn2kÞ; n $ 0; where f is continuous, and the zero solution is assumed to be the unique equilibrium. We focus our discussion on two techniques motivated by stability results for functional differential equ ..."
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Cited by 3 (0 self)
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. We give further insight into the simple ideas that are behind these methods, prove some new results and show applications and open problems.
Equivariant Adaptive Source Separation
 IEEE Trans. on Signal Processing
, 1996
"... Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Eq ..."
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Cited by 449 (9 self)
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(Equivariant Adaptive Separation via Independence) . The EASI algorithms are based on the idea of serial updating: this specific form of matrix updates systematically yields algorithms with a simple, parallelizable structure, for both real and complex mixtures. Most importantly, the performance of an EASI
A Disarmingly Simple Idea? Practical Bottlenecks in Implementing a Universal Basic Income
, 2012
"... A disarmingly simple idea? Practical bottlenecks in the implementation of a universal basic income ..."
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A disarmingly simple idea? Practical bottlenecks in the implementation of a universal basic income
Clustering with Bregman Divergences
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... A wide variety of distortion functions are used for clustering, e.g., squared Euclidean distance, Mahalanobis distance and relative entropy. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergence ..."
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Cited by 443 (57 self)
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generalizing the basic idea to a very large class of clustering loss functions. There are two main contributions in this paper. First, we pose the hard clustering problem in terms of minimizing the loss in Bregman information, a quantity motivated by ratedistortion theory, and present an algorithm to minimize
Results 1  10
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