Institution
Mario Negri Institute for Pharmacological Research
Nonprofit•Milan, 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.
Topics: Population, Risk factor, Cancer, Odds ratio, Breast cancer
Papers published on a yearly basis
Papers
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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
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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
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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
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Theo Vos1, Amanuel Alemu Abajobir, Kalkidan Hassen Abate2, Cristiana Abbafati3 +775 more•Institutions (305)
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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Alberto Mantovani | 183 | 1397 | 163826 |
Giuseppe Remuzzi | 172 | 1226 | 160440 |
Nicholas J. White | 161 | 1352 | 104539 |
Tomas Hökfelt | 158 | 1033 | 95979 |
Silvia Franceschi | 155 | 1340 | 112504 |
Antonio Lanzavecchia | 145 | 408 | 100065 |
Aldo P. Maggioni | 134 | 940 | 90242 |
Eva Negri | 129 | 1010 | 66735 |
Domenico Palli | 128 | 887 | 71478 |
Mark H. Ginsberg | 127 | 474 | 57011 |
James D. Griffin | 124 | 490 | 55565 |
Elisabetta Dejana | 122 | 430 | 48254 |
Jay N. Cohn | 122 | 801 | 86320 |
Barry M. Brenner | 121 | 540 | 65006 |
Michael A. Gimbrone | 120 | 267 | 57423 |