9 citations · 12 across the 6 of their papers we have counts for
6 papers
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…
HOPE: High-order Polynomial Expansion of Black-box Neural Networks
Tingxiong Xiao, Weihang Zhang, Yuxiao Cheng +1
Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring makin…
SHoP: A Deep Learning Framework for Solving High-order Partial Differential Equations
Tingxiong Xiao, Runzhao Yang, Yuxiao Cheng +2
Solving partial differential equations (PDEs) has been a fundamental problem in computational science and of wide applications for both scientific and engineering research. Due to…
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Yuxiao Cheng, Runzhao Yang, Tingxiong Xiao +4
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and hig…
SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng +4
Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression…