Mengying Xu — accepted AI-conference papers

2024

CLEAR: Can Language Models Really Understand Causal Graphs?

Sirui Chen, Mengying Xu, Kun Wang, Xingyu Zeng, Rui Zhao, Shengjie Zhao +1

EMNLP 2024finding

Causal reasoning is a cornerstone of how humans interpret the world. To model and reason about causality, causal graphs offer a concise yet effective solution. Given the impressive advancements in language models, a crucial question arises: can they really understand causal graphs? To this end, we p…

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2017

Sparse inverse bilateral filters for image processing

Akshay Gadde, Mengying Xu, Antonio Ortega

ICASSP 2017accepted

The bilateral filter (BF) is a prominent tool for adaptive, structure-preserving image filtering. It can be interpreted as a graph-based filter, where the nodes of the graph correspond to image pixels and link weights correspond to filter coefficients. Graphs associated to BFs of typical sizes used…

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