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cs.LG2026
A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm
Duy Hoang, Bastien Berret, Olivier Bruneau +1
Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This ability, commonly referred to as…
cs.LG2025
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
Quang H. Nguyen, Thinh Dao, Duy C. Hoang +4
The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and…
cs.LG2025
Momentum Contrastive Learning with Enhanced Negative Sampling and Hard Negative Filtering
Duy Hoang, Huy Ngo, Khoi Pham +3
Contrastive learning has become pivotal in unsupervised representation learning, with frameworks like Momentum Contrast (MoCo) effectively utilizing large negative sample sets to e…