Institution
University of Oxford
Education•Oxford, Oxfordshire, United Kingdom•
About: University of Oxford is a education organization based out in Oxford, Oxfordshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 99713 authors who have published 258108 publications receiving 12972806 citations. The organization is also known as: Oxford University & Oxon..
Topics: Population, Context (language use), Galaxy, Politics, Medicine
Papers published on a yearly basis
Papers
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13 Oct 2015TL;DR: MatConvNet exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more.
Abstract: MatConvNet is an open source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB environment. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more. MatConvNet can be easily extended, often using only MATLAB code, allowing fast prototyping of new CNN architectures. At the same time, it supports efficient computation on CPU and GPU, allowing to train complex models on large datasets such as ImageNet ILSVRC containing millions of training examples
2,989 citations
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TL;DR: In virtually all medical domains, diagnostic and prognostic multivariable prediction models are being developed, validated, updated, and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making.
Abstract: The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
2,982 citations
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TL;DR: Authors/Task Force Members (François Macha, Colin Baigentb,∗∗,2, Alberico L. Catapanoc), ESC Committee for Practice Guidelines (CPG) (Stephan Windeckeraa), ESC National Cardiac Societies (Djamaleddine Nibouchean, Parounak H. Patelcl)
2,972 citations
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TL;DR: A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion, allowing for the quantification of belief in tractography results and the estimation of the cortical connectivity of the human thalamus.
Abstract: A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate.
2,970 citations
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TL;DR: In this paper, a new system LixCoO2 (0 Li x CoO 2 Li ) is proposed, which shows low overvoltages and good reversibility for current densities up to 4 mA cm−2 over a large range of x.
2,960 citations
Authors
Showing all 101421 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
Albert Hofman | 267 | 2530 | 321405 |
Douglas G. Altman | 253 | 1001 | 680344 |
Salim Yusuf | 231 | 1439 | 252912 |
George Davey Smith | 224 | 2540 | 248373 |
Yi Chen | 217 | 4342 | 293080 |
David J. Hunter | 213 | 1836 | 207050 |
Nicholas J. Wareham | 212 | 1657 | 204896 |
Christopher J L Murray | 209 | 754 | 310329 |
Cyrus Cooper | 204 | 1869 | 206782 |
Mark J. Daly | 204 | 763 | 304452 |
David Miller | 203 | 2573 | 204840 |
Mark I. McCarthy | 200 | 1028 | 187898 |
Raymond J. Dolan | 196 | 919 | 138540 |
Frank E. Speizer | 193 | 636 | 135891 |