62 citations · 80 across the 3 of their papers we have counts for
8 papers
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance
Chu Myaet Thwal, Ye Lin Tun, Minh N. H. Nguyen +2
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 resour…
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
Chu Myaet Thwal, Ye Lin Tun, Kitae Kim +2
Recent innovations in transformers have shown their superior performance in natural language processing (NLP) and computer vision (CV). The ability to capture long-range dependenci…
Attention on Personalized Clinical Decision Support System: Federated Learning Approach
Chu Myaet Thwal, Kyi Thar, Ye Lin Tun +1
Health management has become a primary problem as new kinds of diseases and complex symptoms are introduced to a rapidly growing modern society. Building a better and smarter healt…
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning
Chu Myaet Thwal, Minh N. H. Nguyen, Ye Lin Tun +3
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 F…
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
Ye Lin Tun, Chu Myaet Thwal, Ji Su Yoon +3
Diffusion models have shown great potential for vision-related tasks, particularly for image generation. However, their training is typically conducted in a centralized manner, rel…
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Ye Lin Tun, Minh N. H. Nguyen, Chu Myaet Thwal +2
Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suff…