4 citations · 7 across the 9 of their papers we have counts for
10 papers
Fatigue-Aware Learning to Defer via Constrained Optimisation
Zheng Zhang, Cuong C. Nguyen, David Rosewarne +2
Learning to defer (L2D) enables human-AI cooperation by deciding when an AI system should act autonomously or defer to a human expert. Existing L2D methods, however, assume static…
Adaptive Layer-Wise Transformations for Post-Training Quantization of Large Language Models
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +4
Large language models require significant computational resources for deployment, making quantization essential for practical applications. However, the main obstacle to effective…
Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +3
Large language models (LLMs) have significantly advanced natural language processing, but their massive parameter counts create substantial computational and memory challenges duri…
AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
Arpit Garg, Cuong Nguyen, Rafael Felix +3
Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets oft…
Coverage-Constrained Human-AI Cooperation with Multiple Experts
Zheng Zhang, Cuong Nguyen, Kevin Wells +3
Human-AI cooperative classification (HAI-CC) approaches aim to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by levera…
MetaAug: Meta-Data Augmentation for Post-Training Quantization
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +4
Post-Training Quantization (PTQ) has received significant attention because it requires only a small set of calibration data to quantize a full-precision model, which is more pract…