Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Cao, Hongliu"'
Autor:
Cao, Hongliu
Text embedding methods have become increasingly popular in both industrial and academic fields due to their critical role in a variety of natural language processing tasks. The significance of universal text embeddings has been further highlighted wi
Externí odkaz:
http://arxiv.org/abs/2406.01607
Autor:
Cao, Hongliu
To comply with new legal requirements and policies committed to privacy protection, more and more companies start to deploy cross-silo Federated Learning at global scale, where several clients/silos collaboratively train a global model under the coor
Externí odkaz:
http://arxiv.org/abs/2312.14628
Face recognition has been used more and more in real world applications in recent years. However, when the skin color bias is coupled with intra-personal variations like harsh illumination, the face recognition task is more likely to fail, even durin
Externí odkaz:
http://arxiv.org/abs/2312.14544
As the digitization of travel industry accelerates, analyzing and understanding travelers' behaviors becomes increasingly important. However, traveler data frequently exhibit high data sparsity due to the relatively low frequency of user interactions
Externí odkaz:
http://arxiv.org/abs/2312.14533
Autor:
Cao, Hongliu
Les travaux de cette thèse ont été initiés par des problèmes d’apprentissage de données radiomiques. La Radiomique est une discipline médicale qui vise l’analyse à grande échelle de données issues d’imageries médicales traditionnelle
Externí odkaz:
http://www.theses.fr/2019NORMR073/document
Autor:
Cao, Hongliu, Thomas, Eoin
With the digitization of travel industry, it is more and more important to understand users from their online behaviors. However, online travel industry data are more challenging to analyze due to extra sparseness, dispersed user history actions, fas
Externí odkaz:
http://arxiv.org/abs/2102.06687
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to
Externí odkaz:
http://arxiv.org/abs/2007.08377
Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. T
Externí odkaz:
http://arxiv.org/abs/2007.02572
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radiomics is a recent medical imaging field that has shown during the past
Externí odkaz:
http://arxiv.org/abs/1806.07686
Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer
Externí odkaz:
http://arxiv.org/abs/1803.11241