Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Ronghang Zhu"'
Publikováno v:
JMIR AI, Vol 2, p e40167 (2023)
BackgroundArtificial intelligence (AI) applications based on advanced deep learning methods in image recognition tasks can increase efficiency in the monitoring of medication adherence through automation. AI has sparsely been evaluated for the monito
Externí odkaz:
https://doaj.org/article/7c63d8ab3d5248e0b353d9b43ed882db
Publikováno v:
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) ISBN: 9781611977653
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9dd17748f03eab00b32fe83897237af0
https://doi.org/10.1137/1.9781611977653.ch104
https://doi.org/10.1137/1.9781611977653.ch104
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af67ea3f0c6bbdd11790afe289f4de43
https://doi.org/10.1145/3534678.3539243
https://doi.org/10.1145/3534678.3539243
BACKGROUND Artificial intelligence (AI) applications based on advanced deep learning methods in image recognition tasks can increase efficiency in the monitoring of medication adherence through automation. AI has sparsely been evaluated for the monit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b8d4765f7ce8678f7ac80e32b6dd5f7
https://doi.org/10.2196/preprints.40167
https://doi.org/10.2196/preprints.40167
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e964026f6e3ae010a7299b52147e32da
https://doi.org/10.2139/ssrn.4074672
https://doi.org/10.2139/ssrn.4074672
Publikováno v:
SIGIR
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9bde96842fba7e951fa9c454b3c2757
http://arxiv.org/abs/2107.04713
http://arxiv.org/abs/2107.04713
Publikováno v:
2021 IEEE International Conference on Multimedia and Expo (ICME).
Unsupervised domain adaptation has attracted increasing attention in recent years, which adapts classifiers to an unlabeled target domain by exploiting a labeled source domain. To reduce discrepancy between source and target domains, adversarial lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80f8e4534388d0fa0643cad96be82656
https://doi.org/10.1109/icme51207.2021.9428079
https://doi.org/10.1109/icme51207.2021.9428079
Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5436aa1d04d1f74aec4b92faec862ea2
Publikováno v:
CVPR
This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recogn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bb01bc7898a3e9ea1169bdbae06d2b4
https://doi.org/10.1109/cvpr.2018.00547
https://doi.org/10.1109/cvpr.2018.00547
Publikováno v:
ICB
Facial expression recognition is an important problem in many face-related tasks, such as face recognition, face animation, affective computing and human-computer interface. Existing methods mostly assume that testing and training face images are cap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af57e56984508d1f94acbe41942c7478
https://doi.org/10.1109/icb.2016.7550085
https://doi.org/10.1109/icb.2016.7550085