23 citations · 27 across the 6 of their papers we have counts for
6 papers
EyeSeg: An Uncertainty-Aware Eye Segmentation Framework for AR/VR
Zhengyuan Peng, Jianqing Xu, Shen Li +8
Human-machine interaction through augmented reality (AR) and virtual reality (VR) is increasingly prevalent, requiring accurate and efficient gaze estimation which hinges on the ac…
Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection
Jiaying Wu, Shen Li, Ailin Deng +2
Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of new…
Proximity-Informed Calibration for Deep Neural Networks
Miao Xiong, Ailin Deng, Pang Wei Koh +4
Confidence calibration is central to providing accurate and interpretable uncertainty estimates, especially under safety-critical scenarios. However, we find that existing calibrat…
Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness
Ailin Deng, Shen Li, Miao Xiong +2
Trustworthy machine learning is of primary importance to the practical deployment of deep learning models. While state-of-the-art models achieve astonishingly good performance in t…
Neural PCA for Flow-Based Representation Learning
Shen Li, Bryan Hooi
Of particular interest is to discover useful representations solely from observations in an unsupervised generative manner. However, the question of whether existing normalizing fl…
Probabilistic Contrastive Loss for Self-Supervised Learning
Shen Li, Jianqing Xu, Bryan Hooi
This paper proposes a probabilistic contrastive loss function for self-supervised learning. The well-known contrastive loss is deterministic and involves a temperature hyperparamet…