Institution
Carnegie Mellon University
Education•Pittsburgh, Pennsylvania, United States•
About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors present a conceptual model of how social networks impact health, and argue that networks operate at the behavioral level through four primary pathways: (1) provision of social support; (2) social influence; (3) on social engagement and attachment; and (4) access to resources and material goods.
4,033 citations
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TL;DR: A theory of the way working memory capacity constrains comprehension is proposed, which proposes that both processing and storage are mediated by activation and that the total amount of activation available in working memory varies among individuals.
Abstract: A theory of the way working memory capacity constrains comprehension is proposed. The theory proposes that both processing and storage are mediated by activation and that the total amount of activation available in working memory varies among individuals. Individual differences in working memory capacity for language can account for qualitative and quantitative differences among college-age adults in several aspects of language comprehension. One aspect is syntactic modularity: The larger capacity of some individuals permits interaction among syntactic and pragmatic information, so that their syntactic processes are not informationally encapsulated. Another aspect is syntactic ambiguity: The larger capacity of some individuals permits them to maintain multiple interpretations. The theory is instantiated as a production system model in which the amount of activation available to the model affects how it adapts to the transient computational and storage demands that occur in comprehension.
4,000 citations
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TL;DR: The author argues that all 3 variables that assess different aspects of social relationships are associated with health outcomes, that these variables each influence health through different mechanisms, and that associations between these variables and health are not spurious findings attributable to the authors' personalities.
Abstract: The author discusses 3 variables that assess different aspects of social relationships—social support, social integration, and negative interaction. The author argues that all 3 are associated with health outcomes, that these variables each influence health through different mechanisms, and that associations between these variables and health are not spurious findings attributable to our personalities. This argument suggests a broader view of how to intervene in social networks to improve health. This includes facilitating both social integration and social support by creating and nurturing both close (strong) and peripheral (weak) ties within natural social networks and reducing opportunities for negative social interaction. Finally, the author emphasizes the necessity to understand more about who benefits most and least from socialconnectedness interventions.
3,981 citations
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04 Mar 2016TL;DR: Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 of juny 2016.
Abstract: Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.
3,960 citations
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21 Jul 2017TL;DR: Part Affinity Fields (PAFs) as discussed by the authors uses a nonparametric representation to learn to associate body parts with individuals in the image and achieves state-of-the-art performance on the MPII Multi-Person benchmark.
Abstract: We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.
3,958 citations
Authors
Showing all 36645 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Robert C. Nichol | 187 | 851 | 162994 |
Michael I. Jordan | 176 | 1016 | 216204 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
J. N. Butler | 172 | 2525 | 175561 |
P. Chang | 170 | 2154 | 151783 |
Krzysztof Matyjaszewski | 169 | 1431 | 128585 |
Yang Yang | 164 | 2704 | 144071 |
Geoffrey E. Hinton | 157 | 414 | 409047 |
Herbert A. Simon | 157 | 745 | 194597 |
Yongsun Kim | 156 | 2588 | 145619 |
Terrence J. Sejnowski | 155 | 845 | 117382 |
John B. Goodenough | 151 | 1064 | 113741 |
Scott Shenker | 150 | 454 | 118017 |