Domain adaptation with conditional transferable components
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …
Domain Adaptation with Conditional Transferable Components
M Gong, K Zhang, T Liu, D Tao… - JMLR workshop …, 2016 - pubmed.ncbi.nlm.nih.gov
Domain adaptation arises in supervised learning when the training (source domain) and test
(target domain) data have different distributions. Let X and Y denote the features and target …
(target domain) data have different distributions. Let X and Y denote the features and target …
[PDF][PDF] Domain Adaptation with Conditional Transferable Components
M Gong, S UTS, K Zhang, EDUT Liu, D Tao, UTS EDU… - mingming-gong.github.io
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …
Domain adaptation with conditional transferable components
M Gong, K Zhang, T Liu, D Tao, C Glymour… - Proceedings of the 33rd …, 2016 - dl.acm.org
Domain adaptation arises in supervised learning when the training (source domain) and test
(target domain) data have different distributions. Let X and Y denote the features and target …
(target domain) data have different distributions. Let X and Y denote the features and target …
[HTML][HTML] Domain Adaptation with Conditional Transferable Components
M Gong, K Zhang, T Liu, D Tao, C Glymour… - JMLR workshop and …, 2016 - ncbi.nlm.nih.gov
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …
[PDF][PDF] Domain Adaptation with Conditional Transferable Components
M Gong, S UTS, K Zhang, EDUT Liu, D Tao, UTS EDU… - utstat.toronto.edu
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …
Domain adaptation with conditional transferable components
M Gong, K Zhang, T Liu, D Tao… - … Learning, ICML 2016, 2016 - opus.lib.uts.edu.au
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …
Domain Adaptation with Conditional Transferable Components
M Gong, K Zhang, T Liu, D Tao… - International …, 2016 - proceedings.mlr.press
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …
Domain Adaptation with Conditional Transferable Components.
M Gong, K Zhang, T Liu, D Tao, C Glymour… - JMLR Workshop and …, 2016 - europepmc.org
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …
[PDF][PDF] Domain Adaptation with Conditional Transferable Components
M Gong, S UTS, K Zhang, EDUT Liu, D Tao, UTS EDU… - utstat.utoronto.ca
Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …
and test (target domain) data have different distributions. Let X and Y denote the features …