8 citations · 8 across the 3 of their papers we have counts for
6 papers
Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting
Chenhua Shi, Bhavika Jalli, John Zou +4
Telecom troubleshooting at edge sites requires low-latency model responses and localized model adaptation to satisfy operational and data sovereignty requirements. However, deployi…
Dyna-DINO: Efficient ViT Distillation Via Adaptive Representation Anchoring
Jiaqi Zhang, Ashton Lee, Anthony Wong +3
Vision Foundation Models (VFMs) with Vision Transformer (ViT) backbones, such as DINOv2, have become essential for downstream tasks like object recognition and semantic segmentatio…
Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting
Chenhua Shi, Bhavika Jalli, Gregor Macdonald +4
Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging. Although Artificial Intelligence (A…
Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications
Chenhua Shi, Gregor Macdonald, Bhavika Jalli +4
The success of large language models (LLMs) depends heavily on large-scale, high-quality instruction-following and reinforcement datasets. However, generating such data through hum…
FastDINOv2: Frequency Based Curriculum Learning Improves Robustness and Training Speed
Jiaqi Zhang, Juntuo Wang, Zhixin Sun +2
Large-scale vision foundation models such as DINOv2 boast impressive performances by leveraging massive architectures and training datasets. But numerous scenarios require practiti…
Agentic LLM Workflows for Generating Patient-Friendly Medical Reports
Malavikha Sudarshan, Sophie Shih, Estella Yee +7
The application of Large Language Models (LLMs) in healthcare is expanding rapidly, with one potential use case being the translation of formal medical reports into patient-legible…