Institution
The Chinese University of Hong Kong
Education•Hong Kong, China•
About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.
Topics: Population, Computer science, Cancer, Medicine, China
Papers published on a yearly basis
Papers
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07 Dec 2015TL;DR: A novel deep learning framework for attribute prediction in the wild that cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently.
Abstract: Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.
6,273 citations
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TL;DR: Zhang et al. as discussed by the authors proposed a deep learning method for single image super-resolution (SR), which directly learns an end-to-end mapping between the low/high-resolution images.
Abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
6,122 citations
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Baylor College of Medicine1, Chinese Academy of Sciences2, Chinese National Human Genome Center3, University of Hong Kong4, The Chinese University of Hong Kong5, Hong Kong University of Science and Technology6, Illumina7, McGill University8, Washington University in St. Louis9, University of California, San Francisco10, Wellcome Trust Sanger Institute11, Beijing Normal University12, Health Sciences University of Hokkaido13, Shinshu University14, University of Tsukuba15, Howard University16, University of Ibadan17, Case Western Reserve University18, University of Utah19, Cold Spring Harbor Laboratory20, Johns Hopkins University21, University of Oxford22, North Carolina State University23, National Institutes of Health24, Massachusetts Institute of Technology25, Chinese Academy of Social Sciences26, Kyoto University27, Nagasaki University28, Wellcome Trust29, Genome Canada30, Foundation for the National Institutes of Health31, University of Maryland, Baltimore32, Vanderbilt University33, Stanford University34, University of California, Berkeley35, New York University36, University of Oklahoma37, University of New Mexico38, Université de Montréal39, University of California, Los Angeles40, University of Michigan41, University of Wisconsin-Madison42, London School of Economics and Political Science43, Genetic Alliance44, GlaxoSmithKline45, University of Washington46, Harvard University47, University of Chicago48, Fred Hutchinson Cancer Research Center49, University of Tokyo50
TL;DR: The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance the ability to choose targets for therapeutic intervention.
Abstract: The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.
5,926 citations
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27 Oct 2005
TL;DR: A public database of common variation in the human genome: more than one million single nucleotide polymorphisms for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted.
Abstract: Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution.
5,479 citations
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Gregory A. Roth1, Gregory A. Roth2, Degu Abate3, Kalkidan Hassen Abate4 +1025 more•Institutions (333)
TL;DR: Non-communicable diseases comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2).
5,211 citations
Authors
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Name | H-index | Papers | Citations |
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Michael Marmot | 193 | 1147 | 170338 |
Jing Wang | 184 | 4046 | 202769 |
Jiaguo Yu | 178 | 730 | 113300 |
Yang Yang | 171 | 2644 | 153049 |
Mark Gerstein | 168 | 751 | 149578 |
Gang Chen | 167 | 3372 | 149819 |
Jun Wang | 166 | 1093 | 141621 |
Jean Louis Vincent | 161 | 1667 | 163721 |
Wei Zheng | 151 | 1929 | 120209 |
Rui Zhang | 151 | 2625 | 107917 |
Ben Zhong Tang | 149 | 2007 | 116294 |
Kypros H. Nicolaides | 147 | 1302 | 87091 |
Thomas S. Huang | 146 | 1299 | 101564 |
Galen D. Stucky | 144 | 958 | 101796 |
Joseph J.Y. Sung | 142 | 1240 | 92035 |