Institution
National Research University – Higher School of Economics
Education•Moscow, Russia•
About: National Research University – Higher School of Economics is a education organization based out in Moscow, Russia. It is known for research contribution in the topics: Population & Politics. The organization has 12873 authors who have published 23376 publications receiving 256396 citations.
Papers published on a yearly basis
Papers
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Nicholas J Kassebaum1, Ryan M Barber1, Zulfiqar A Bhutta2, Zulfiqar A Bhutta3 +613 more•Institutions (272)
TL;DR: In this article, the authors quantified maternal mortality throughout the world by underlying cause and age from 1990 to 2015 for ages 10-54 years by systematically compiling and processing all available data sources from 186 of 195 countries and territories.
641 citations
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University of Jyväskylä1, California Polytechnic State University2, University of California, Los Angeles3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, Brigham Young University12, University of Texas at Austin13, Universidade Federal de Minas Gerais14, Google15
TL;DR: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
617 citations
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Haidong Wang1, Zulfiqar A Bhutta2, Zulfiqar A Bhutta3, Matthew M Coates1 +610 more•Institutions (263)
TL;DR: The Global Burden of Disease 2015 Study provides an analytical framework to comprehensively assess trends for under-5 mortality, age-specific and cause-specific mortality among children under 5 years, and stillbirths by geography over time and decomposed the changes in under- 5 mortality to changes in SDI at the global level.
591 citations
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TL;DR: This paper converts the dense weight matrices of the fully-connected layers to the Tensor Train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved.
Abstract: Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved. In particular, for the Very Deep VGG networks we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times.
588 citations
01 Jan 2012
540 citations
Authors
Showing all 13307 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rasmus Nielsen | 135 | 556 | 84898 |
Matthew Jones | 125 | 1161 | 96909 |
Fedor Ratnikov | 123 | 1104 | 67091 |
Kenneth J. Arrow | 113 | 411 | 111221 |
Wil M. P. van der Aalst | 108 | 725 | 42429 |
Peter Schmidt | 105 | 638 | 61822 |
Roel Aaij | 98 | 1071 | 44234 |
John W. Berry | 97 | 351 | 52470 |
Federico Alessio | 96 | 1054 | 42300 |
Denis Derkach | 96 | 1184 | 45772 |
Marco Adinolfi | 95 | 831 | 40777 |
Michael Alexander | 95 | 881 | 38749 |
Alexey Boldyrev | 94 | 439 | 32000 |
Shalom H. Schwartz | 94 | 220 | 67609 |
Richard Blundell | 93 | 487 | 61730 |