Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Mark Cheung"'
Autor:
Mark Cheung, Ron Ekers, John Morgan, Rajan Chhetri, Angelica Waszewski, George Hobbs, Dilpreet Kaur, Andrew Zic, Ramesh Bhat, Meng Jin
CSIRO, Australia's national science agency, operates a number of world-class radio astronomy observatories that are collectively known as the Australia Telescope National Facility (ATNF). The facility offers a powerful view of the southern hemisphere
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9961f571cae3dd88b7cd4e8cb48cc777
https://doi.org/10.5194/egusphere-egu23-15289
https://doi.org/10.5194/egusphere-egu23-15289
Publikováno v:
IEEE Signal Processing Magazine. 37:139-149
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures perform poorly when data have an underlying graph stru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e706e6f91435ca398457fe2324afa076
https://doi.org/10.1109/msp.2020.3014594
https://doi.org/10.1109/msp.2020.3014594
Publikováno v:
2021 55th Asilomar Conference on Signals, Systems, and Computers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34ea6f10a4d4f1ae29b09283525918fd
https://doi.org/10.1109/ieeeconf53345.2021.9723317
https://doi.org/10.1109/ieeeconf53345.2021.9723317
Autor:
Mark Cheung, Jose M.F. Moura
Publikováno v:
2021 55th Asilomar Conference on Signals, Systems, and Computers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::76acc842867f0aa56cfc7bf108f36b9f
https://doi.org/10.1109/ieeeconf53345.2021.9723353
https://doi.org/10.1109/ieeeconf53345.2021.9723353
Publikováno v:
ACSSC
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data. A question that arises is how much performance improvement does the underlying graph structure in the GNN provide over the CNN (that ignores this graph stru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c74863132e7e683b8d9986599ed66216
http://arxiv.org/abs/2012.08698
http://arxiv.org/abs/2012.08698
Autor:
Jose M. F. Moura, Mark Cheung
Publikováno v:
IEEE BigData
Deep learning has led to major advances in fields like natural language processing, computer vision, and other Euclidean data domains. Yet, many important fields have data defined on irregular domains, requiring graphs to be explicitly modeled. One s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c980b9b63184785d2914c118d3845e67
https://doi.org/10.1109/bigdata50022.2020.9378164
https://doi.org/10.1109/bigdata50022.2020.9378164
Publikováno v:
ACSSC
Convolutional neural networks (CNNs) have been very successful with learning on grid-based data such as time series and images. However, traditional CNNs do not perform well on irregular-structured data defined on a graph. Graph convolutional neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ead02b997436743bc2cdfa49d1badc20
https://doi.org/10.1109/ieeeconf51394.2020.9443440
https://doi.org/10.1109/ieeeconf51394.2020.9443440
Publikováno v:
ACSSC
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods for GCNNs, and combinations of those graph pooling methods with three
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f83e1650731fd3b6a69bfbb52755b7cc
http://arxiv.org/abs/2004.03519
http://arxiv.org/abs/2004.03519
Autor:
Han-Chih Hencher Lee, TH Tsoi, Shun Wong, Ying-Kit Frank Leung, Tina Yee-Ching Chan, Chloe Miu Mak, Tze-Chin Gene Lau, Hok-Fung Tong, Bun Sheng, Richard Li, Chi-Nam Lee, Ka-Ho Lee, Luen-Cheung Ho, Ling-Yin Esther Hung, Tin-Wing Tong, Yuk-Fai Nelson Cheung, Yim-Pui Chu, Ching-Wan Lam, Yue Sandy Cheng, Siu-Hung Li, Chi Terence Li, Nin-Yuan Keith Pan, Albert Yan-Wo Chan, Nim-Chi Amanda Kan, Yat-Pang Michael Fu, Kwok-kwong Lau, Sung-Yan Sue Yeung, Ho-Ying Cory Leung, Hon-Ming Jonas Yeung, Chi-Fung Mark Cheung, Wing-Chi Lisa Au, Sau-Yin Blanka Lee, Tsz-Yan Tammy Tong
Publikováno v:
Clinical geneticsREFERENCES. 97(5)
FLNC-related myofibrillar myopathy could manifest as autosomal dominant late-onset slowly progressive proximal muscle weakness; involvements of cardiac and/or respiratory functions are common. We describe 34 patients in nine families of FLNC-related
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab3bd6a20101194a2d2880bc7f861196
https://pubmed.ncbi.nlm.nih.gov/32022900
https://pubmed.ncbi.nlm.nih.gov/32022900
Publikováno v:
The Journal of emergency medicine. 58(2)
Background Methemoglobinemia and carbon monoxide poisoning are potentially life-threatening conditions that can present with nonspecific clinical features. This lack of specificity increases the probability of misdiagnosis or avoidable delays in diag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::768020b911996850ada15acad55239c2
https://pubmed.ncbi.nlm.nih.gov/31740155
https://pubmed.ncbi.nlm.nih.gov/31740155