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Institution

Mario Negri Institute for Pharmacological Research

NonprofitMilan, Italy
About: Mario Negri Institute for Pharmacological Research is a nonprofit organization based out in Milan, Italy. It is known for research contribution in the topics: Population & Risk factor. The organization has 6034 authors who have published 13216 publications receiving 829126 citations. The organization is also known as: Istituto di ricerche farmacologiche Mario Negri & marionegri.it.


Papers
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Journal ArticleDOI
TL;DR: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is introduced, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses.
Abstract: Moher and colleagues introduce PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses. Us...

23,203 citations

Posted Content
TL;DR: PyTorch as discussed by the authors is a machine learning library that provides an imperative and Pythonic programming style that makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs.
Abstract: Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.

12,767 citations

Journal ArticleDOI
Rafael Lozano1, Mohsen Naghavi1, Kyle J Foreman2, Stephen S Lim1  +192 moreInstitutions (95)
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex, using the Cause of Death Ensemble model.

11,809 citations

Journal ArticleDOI
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.

10,401 citations

Proceedings Article
01 Jan 2019
TL;DR: This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.
Abstract: Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.

10,045 citations


Authors

Showing all 6060 results

NameH-indexPapersCitations
Alberto Mantovani1831397163826
Giuseppe Remuzzi1721226160440
Nicholas J. White1611352104539
Tomas Hökfelt158103395979
Silvia Franceschi1551340112504
Antonio Lanzavecchia145408100065
Aldo P. Maggioni13494090242
Eva Negri129101066735
Domenico Palli12888771478
Mark H. Ginsberg12747457011
James D. Griffin12449055565
Elisabetta Dejana12243048254
Jay N. Cohn12280186320
Barry M. Brenner12154065006
Michael A. Gimbrone12026757423
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202312
202230
2021505
2020468
2019424
2018386