1 citations · 2 across the 6 of their papers we have counts for
6 papers
ExBind: A Controlled Diagnostic Benchmark for Visual-to-Executable Correspondence
Ziqian Wang, Yuxiao Cheng, Tingxiong Xiao +1
Multimodal coding and editing systems must map a visible or semantic referent to the exact executable object that can be edited. A wrong reference may select a valid but incorrect…
EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series
Ziqian Wang, Tingxiong Xiao, Yuxiao Cheng +1
Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to inefficient representations. We prop…
A Self-explainable Model of Long Time Series by Extracting Informative Structured Causal Patterns
Ziqian Wang, Yuxiao Cheng, Jinli Suo
Explainability is essential for neural networks that model long time series, yet most existing explainable AI methods only produce point-wise importance scores and fail to capture…
A Unified Data-Driven Framework for Efficient Scientific Discovery
Tingxiong Xiao, Xinxin Song, Ziqian Wang +2
Scientific discovery drives progress across disciplines, from fundamental physics to industrial applications. However, identifying physical laws automatically from gathered dataset…
Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Yuxiao Cheng, Xinxin Song, Ziqian Wang +3
Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteriorations such as acute kidney in…
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao +3
Time-series causal discovery (TSCD) is a fundamental problem of machine learning. However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performan…