3 papers
cs.LG2026
Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models
Dung Anh Hoang, Cuong Pham, Cuong Nguyen +3
Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but their massive sizes hinder deployment on resource-constrained devices. To reduce their…
cs.LG2026
Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models
Dung Anh Hoang, Cuong Pham anh Trung Le, Jianfei Cai +1
Diffusion models have shown remarkable performance in image synthesis by progressively estimating a smooth transition from a Gaussian distribution of noise to a real image. Unfortu…
cs.LG2025
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
Dung Anh Hoang, Cuong Nguyen, Belagiannis Vasileios +2
Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced…