15 citations · 23 across the 5 of their papers we have counts for
5 papers
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs
Flint Xiaofeng Fan, Cheston Tan, Roger Wattenhofer +1
The rise of reinforcement learning (RL) in critical real-world applications demands a fundamental rethinking of privacy in AI systems. Traditional privacy frameworks, designed to p…
Human-Generative AI Collaborative Problem Solving Who Leads and How Students Perceive the Interactions
Gaoxia Zhu, Vidya Sudarshan, Jason Fok Kow +1
This research investigates distinct human-generative AI collaboration types and students' interaction experiences when collaborating with generative AI (i.e., ChatGPT) for problem-…
Unsupervised Object-Level Representation Learning from Scene Images
Jiahao Xie, Xiaohang Zhan, Ziwei Liu +2
Contrastive self-supervised learning has largely narrowed the gap to supervised pre-training on ImageNet. However, its success highly relies on the object-centric priors of ImageNe…
Learning Conjoint Attentions for Graph Neural Nets
Tiantian He, Yew-Soon Ong, Lu Bai
In this paper, we present Conjoint Attentions (CAs), a class of novel learning-to-attend strategies for graph neural networks (GNNs). Besides considering the layer-wise node featur…
Delving into Inter-Image Invariance for Unsupervised Visual Representations
Jiahao Xie, Xiaohang Zhan, Ziwei Liu +2
Contrastive learning has recently shown immense potential in unsupervised visual representation learning. Existing studies in this track mainly focus on intra-image invariance lear…