3 citations · 8 across the 8 of their papers we have counts for
9 papers
Early Stopping in Large Reasoning Models: A Survey and Empirical Audit of Efficiency and Reliability
Quang Hung Nguyen, Hanh Nguyen, Thanh Tam Nguyen +3
Large reasoning models can improve accuracy by generating longer reasoning traces, but some of this computation may be unnecessary. Early-stopping methods aim to stop once enough r…
Decoding the Critique Mechanism in Large Reasoning Models
Hoang Phan, Quang H. Nguyen, Hung T. Q. Le +3
Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong perfo…
Unveiling Concept Attribution in Diffusion Models
Quang H. Nguyen, Hoang Phan, Khoa D. Doan
Diffusion models have shown remarkable abilities in generating realistic and high-quality images from text prompts. However, a trained model remains largely black-box; little do we…
Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks
Quang H. Nguyen, Nguyen Ngoc-Hieu, The-Anh Ta +4
Deep neural networks are vulnerable to backdoor attacks, a type of adversarial attack that poisons the training data to manipulate the behavior of models trained on such data. Clea…
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…
Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models
Sze Jue Yang, Chinh D. La, Quang H. Nguyen +4
Backdoor attacks, representing an emerging threat to the integrity of deep neural networks, have garnered significant attention due to their ability to compromise deep learning sys…
Fooling the Textual Fooler via Randomizing Latent Representations
Duy C. Hoang, Quang H. Nguyen, Saurav Manchanda +3
Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cau…
Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
Quang H. Nguyen, Yingjie Lao, Tung Pham +2
Recent works have shown that deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify. Even with…
Under-confidence Backdoors Are Resilient and Stealthy Backdoors
Minlong Peng, Zidi Xiong, Quang H. Nguyen +3
By injecting a small number of poisoned samples into the training set, backdoor attacks aim to make the victim model produce designed outputs on any input injected with pre-designe…