47 citations · 78 across the 19 of their papers we have counts for
5 papers · 1 filter
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
Shirley Wu, Kaidi Cao, Bruno Ribeiro +2
Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning archite…
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
Chenhang Cui, Yiyang Zhou, Xinyu Yang +4
While GPT-4V(ision) impressively models both visual and textual information simultaneously, it's hallucination behavior has not been systematically assessed. To bridge this gap, we…
Communication-Free Distributed GNN Training with Vertex Cut
Kaidi Cao, Rui Deng, Shirley Wu +3
Training Graph Neural Networks (GNNs) on real-world graphs consisting of billions of nodes and edges is quite challenging, primarily due to the substantial memory needed to store t…
Med-Flamingo: a Multimodal Medical Few-shot Learner
Michael Moor, Qian Huang, Shirley Wu +6
Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a firs…
Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Shirley Wu, Mert Yuksekgonul, Linjun Zhang +1
Deep neural networks often rely on spurious correlations to make predictions, which hinders generalization beyond training environments. For instance, models that associate cats wi…