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Institution

National Research University – Higher School of Economics

EducationMoscow, 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 & Computer science. The organization has 12873 authors who have published 23376 publications receiving 256396 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the effect on return on investment (ROI) of the use of social media networks (SMNs) as external drivers for supporting internal innovation search processes is investigated.

122 citations

Journal ArticleDOI
TL;DR: In this paper, the Coulomb branches of unframed and framed quiver gauge theories of type $ADE were studied and shown to be isomorphic to the moduli space of based rational maps from the flag variety.
Abstract: This is a companion paper of arXiv:1601.03586. We study Coulomb branches of unframed and framed quiver gauge theories of type $ADE$. In the unframed case they are isomorphic to the moduli space of based rational maps from ${\\mathbb C}P^1$ to the flag variety. In the framed case they are slices in the affine Grassmannian and their generalization. In the appendix, written jointly with Joel Kamnitzer, Ryosuke Kodera, Ben Webster, and Alex Weekes, we identify the quantized Coulomb branch with the truncated shifted Yangian.

120 citations

Journal ArticleDOI
TL;DR: This paper explored the effect of diverse firm resources and competences such as founders' human capital, workforce human capital and acquisition of knowledge from external sources on the innovation performance of young firms.

120 citations

Proceedings Article
03 Jul 2018
TL;DR: In this article, two algorithms for approximating the general optimal transport (OT) distance between two discrete distributions of size n, up to accuracy δ(n 2 ) were presented.
Abstract: We analyze two algorithms for approximating the general optimal transport (OT) distance between two discrete distributions of size $n$, up to accuracy $\varepsilon$. For the first algorithm, which is based on the celebrated Sinkhorn's algorithm, we prove the complexity bound $\widetilde{O}\left({n^2/\varepsilon^2}\right)$ arithmetic operations. For the second one, which is based on our novel Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD) algorithm, we prove the complexity bound $\widetilde{O}\left(\min\left\{n^{9/4}/\varepsilon, n^{2}/\varepsilon^2 \right\}\right)$ arithmetic operations. Both bounds have better dependence on $\varepsilon$ than the state-of-the-art result given by $\widetilde{O}\left({n^2/\varepsilon^3}\right)$. Our second algorithm not only has better dependence on $\varepsilon$ in the complexity bound, but also is not specific to entropic regularization and can solve the OT problem with different regularizers.

120 citations


Authors

Showing all 13307 results

NameH-indexPapersCitations
Rasmus Nielsen13555684898
Matthew Jones125116196909
Fedor Ratnikov123110467091
Kenneth J. Arrow113411111221
Wil M. P. van der Aalst10872542429
Peter Schmidt10563861822
Roel Aaij98107144234
John W. Berry9735152470
Federico Alessio96105442300
Denis Derkach96118445772
Marco Adinolfi9583140777
Michael Alexander9588138749
Alexey Boldyrev9443932000
Shalom H. Schwartz9422067609
Richard Blundell9348761730
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023129
2022586
20212,478
20203,025
20192,590
20182,259