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Institution

Technion – Israel Institute of Technology

EducationHaifa, Israel
About: Technion – Israel Institute of Technology is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Upper and lower bounds. The organization has 31714 authors who have published 79377 publications receiving 2603976 citations. The organization is also known as: Technion Israel Institute of Technology & Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel.


Papers
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Journal ArticleDOI
TL;DR: The aim of this paper is to introduce a few key notions and applications connected to sparsity, targeting newcomers interested in either the mathematical aspects of this area or its applications.
Abstract: A full-rank matrix ${\bf A}\in \mathbb{R}^{n\times m}$ with $n

2,372 citations

Journal ArticleDOI
TL;DR: This tutorial presents the CE methodology, the basic algorithm and its modifications, and discusses applications in combinatorial optimization and machine learning.
Abstract: The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss applications in combinatorial optimization and machine learning.

2,367 citations

Book
01 Jan 2004
TL;DR: This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system and shows that similar stability is also available using the basis and the matching pursuit algorithms.
Abstract: Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system. Considering an ideal underlying signal that has a sufficiently sparse representation, it is assumed that only a noisy version of it can be observed. Assuming further that the overcomplete system is incoherent, it is shown that the optimally sparse approximation to the noisy data differs from the optimally sparse decomposition of the ideal noiseless signal by at most a constant multiple of the noise level. As this optimal-sparsity method requires heavy (combinatorial) computational effort, approximation algorithms are considered. It is shown that similar stability is also available using the basis and the matching pursuit algorithms. Furthermore, it is shown that these methods result in sparse approximation of the noisy data that contains only terms also appearing in the unique sparsest representation of the ideal noiseless sparse signal.

2,365 citations

Journal ArticleDOI
TL;DR: Treatment of women with acute uncomplicated cystitis and pyelonephritis is limited in these guidelines to premenopausal, non-pregnant women with no known urological abnormalities or co-morbidities.
Abstract: A Panel of International Experts was convened by the Infectious Diseases Society of America (IDSA) in collaboration with the European Society for Microbiology and Infectious Diseases (ESCMID) to update the 1999 Uncomplicated Urinary Tract Infection Guidelines by the IDSA. Co-sponsoring organizations include the American Congress of Obstetricians and Gynecologists, American Urological Association, Association of Medical Microbiology and Infectious Diseases-Canada, and the Society for Academic Emergency Medicine. The focus of this work is treatment of women with acute uncomplicated cystitis and pyelonephritis, diagnoses limited in these guidelines to premenopausal, non-pregnant women with no known urological abnormalities or co-morbidities. The issues of in vitro resistance prevalence and the ecological adverse effects of antimicrobial therapy (collateral damage) were considered as important factors in making optimal treatment choices and thus are reflected in the rankings of recommendations.

2,320 citations

Proceedings Article
06 Jan 2007
TL;DR: This work proposes Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia that results in substantial improvements in correlation of computed relatedness scores with human judgments.
Abstract: Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r = 0.56 to 0.75 for individual words and from r = 0.60 to 0.72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

2,285 citations


Authors

Showing all 31937 results

NameH-indexPapersCitations
Robert Langer2812324326306
Nicholas G. Martin1921770161952
Tobin J. Marks1591621111604
Grant W. Montgomery157926108118
David Eisenberg156697112460
David J. Mooney15669594172
Dirk Inzé14964774468
Jerrold M. Olefsky14359577356
Joseph J.Y. Sung142124092035
Deborah Estrin135562106177
Bruce Yabsley133119184889
Jerry W. Shay13363974774
Richard N. Bergman13047791718
Shlomit Tarem129130686919
Allen Mincer129104080059
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023147
2022390
20213,397
20203,526
20193,273
20183,131