Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Minh, P. H."'
Beyond the success of Contrastive Language-Image Pre-training (CLIP), recent trends mark a shift toward exploring the applicability of lightweight vision-language models for resource-constrained scenarios. These models often deliver suboptimal perfor
Externí odkaz:
http://arxiv.org/abs/2412.03871
Combining different data modalities enables deep neural networks to tackle complex tasks more effectively, making multimodal learning increasingly popular. To harness multimodal data closer to end users, it is essential to integrate multimodal learni
Externí odkaz:
http://arxiv.org/abs/2407.15426
We consider Riesz energy problems with radial external fields. We study the question of whether or not the equilibrium is the uniform distribution on a sphere. We develop general necessary as well as general sufficient conditions on the external fiel
Externí odkaz:
http://arxiv.org/abs/2405.00120
Multimodal federated learning (MFL) has emerged as a decentralized machine learning paradigm, allowing multiple clients with different modalities to collaborate on training a machine learning model across diverse data sources without sharing their pr
Externí odkaz:
http://arxiv.org/abs/2401.13898
Autor:
Thwal, Chu Myaet, Nguyen, Minh N. H., Tun, Ye Lin, Kim, Seong Tae, Thai, My T., Hong, Choong Seon
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of FL hinges on the efficiency of participating models and their ability t
Externí odkaz:
http://arxiv.org/abs/2401.11652
Many studies integrate federated learning (FL) with self-supervised learning (SSL) to take advantage of raw data distributed across edge devices. However, edge devices often struggle with high computation and communication costs imposed by SSL and FL
Externí odkaz:
http://arxiv.org/abs/2401.11647
Autor:
Le, Huy Q., Nguyen, Minh N. H., Thwal, Chu Myaet, Qiao, Yu, Zhang, Chaoning, Hong, Choong Seon
Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data. Most existing works simply propose typical FL systems for single-mo
Externí odkaz:
http://arxiv.org/abs/2307.13214
Autor:
Nguyen, Loc X., Tun, Ye Lin, Tun, Yan Kyaw, Nguyen, Minh N. H., Zhang, Chaoning, Han, Zhu, Hong, Choong Seon
Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. Howev
Externí odkaz:
http://arxiv.org/abs/2307.03402
In a practical setting, how to enable robust Federated Learning (FL) systems, both in terms of generalization and personalization abilities, is one important research question. It is a challenging issue due to the consequences of non-i.i.d. propertie
Externí odkaz:
http://arxiv.org/abs/2204.01542
Autor:
Khanh M. Chau, Abishai Dominic, Eleanor L. Davis, Sivareddy Kotla, Estefani Turcios Berrios, Arsany Fahim, Ashwin Arunesh, Shengyu Li, Dongyu Zhao, Kaifu Chen, Alan R. Davis, Minh T. H. Nguyen, Yongxing Wang, Scott E. Evans, Guangyu Wang, John P. Cooke, Jun-ichi Abe, David P. Huston, Nhat-Tu Le
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 10 (2024)
BackgroundTraf2 and Nck-interacting kinase (TNIK) is known for its regulatory role in various processes within cancer cells. However, its role within endothelial cells (ECs) has remained relatively unexplored.MethodsLeveraging RNA-seq data and Ingenu
Externí odkaz:
https://doaj.org/article/8f5bf8c0dfad4de7bc1d2a5c762d2c0e
