Publications (5)
New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class
Yanyun Wang, Li Liu, Zi Liang +4
Adversarial Training (AT) is one of the most effective methods to enhance the robustness of Deep Neural Networks (DNNs). However, existing AT methods suffer from an inherent accura…
Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection
Minseok Jung, Cynthia Fuertes Panizo, Liam Dugan +4
The advancement of large language models (LLMs) has made it difficult to differentiate human-written text from AI-generated text. Several AI-text detectors have been developed in r…
CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonese
Dazhong Chen, Yi-Cheng Lin, Yuchen Huang +5
Automatic speech recognition (ASR) is critical for language accessibility, yet low-resource Cantonese remains challenging due to limited annotated data, six lexical tones, tone san…
Entropy Centroids as Intrinsic Rewards for Test-Time Scaling
Wenshuo Zhao, Qi Zhu, Xingshan Zeng +4
An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existi…
Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems
Xin Ju, Nok Hei, Fung +6
The Earth's subsurface is a cornerstone of modern society, providing essential energy resources like hydrocarbons, geothermal, and minerals while serving as the primary reservoir f…