5 citations · 10 across the 11 of their papers we have counts for
5 papers · 1 filter
How Interpretable Are Interpretable Graph Neural Networks?
Yongqiang Chen, Yatao Bian, Bo Han +1
Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data. Existing XGNNs predominantly adopt the attention…
A Sober Look at the Robustness of CLIPs to Spurious Features
Qizhou Wang, Yong Lin, Yongqiang Chen +3
Large vision language models, such as CLIP, demonstrate impressive robustness to spurious features than single-modal models trained on ImageNet. However, existing test datasets are…
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
Chenxi Liu, Yongqiang Chen, Tongliang Liu +4
Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice…
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations
Binghui Xie, Yatao Bian, Kaiwen zhou +5
Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodolog…
Enhancing Evolving Domain Generalization through Dynamic Latent Representations
Binghui Xie, Yongqiang Chen, Jiaqi Wang +4
Domain generalization is a critical challenge for machine learning systems. Prior domain generalization methods focus on extracting domain-invariant features across several station…