2 citations · 5 across the 8 of their papers we have counts for
9 papers
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction
Tianyu Pang, Yujie Fang, Zihang Liu +4
Muon has recently shown promising results in LLM training. In this work, we study how to further improve Muon. We argue that Muon's orthogonalized update rule suppresses the emerge…
Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
Lei Hsiung, Tianyu Pang, Yung-Chen Tang +4
Recent advancements in large language models (LLMs) have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. Howe…
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models
Xuyuan Liu, Lei Hsiung, Yaoqing Yang +1
Understanding how feature representations evolve across layers in large language models (LLMs) is key to improving their interpretability and robustness. While recent studies have…
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen +1
Adapting pre-trained models to new tasks can exhibit varying effectiveness across datasets. Visual prompting, a state-of-the-art parameter-efficient transfer learning method, can s…
AutoVP: An Automated Visual Prompting Framework and Benchmark
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen +2
Visual prompting (VP) is an emerging parameter-efficient fine-tuning approach to adapting pre-trained vision models to solve various downstream image-classification tasks. However,…
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
Hao-Lun Sun, Lei Hsiung, Nandhini Chandramoorthy +2
Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high. An effective strategy for reducing such consumpti…
NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
Lei Hsiung, Yung-Chen Tang, Pin-Yu Chen +1
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks. However, a lack of consid…
CARBEN: Composite Adversarial Robustness Benchmark
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen +1
Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, mu…
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen +1
Model robustness against adversarial examples of single perturbation type such as the -norm has been widely studied, yet its generalization to more realistic scenarios in…