1 citations · 2 across the 8 of their papers we have counts for
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PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
Ziwen Kan, Wugeng Zheng, Tianlong Chen +1
In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire resp…
MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
Wugeng Zheng, Ziwen Kan, Tianlong Chen +3
Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two failure modes are common: modali…
Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
Wugeng Zheng, Ziwen Kan, Katie Wang +2
Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-modality missingness caused by…
Implet: A Post-hoc Subsequence Explainer for Time Series Models
Fanyu Meng, Ziwen Kan, Shahbaz Rezaei +3
Explainability in time series models is crucial for fostering trust, facilitating debugging, and ensuring interpretability in real-world applications. In this work, we introduce Im…
Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
Ziwen Kan, Shahbaz Rezaei, Xin Liu
The popularity of deep learning methods in the time series domain boosts interest in interpretability studies, including counterfactual (CF) methods. CF methods identify minimal ch…
CHAOSMINING: Benchmarking Post-Hoc Attribution with Sparse Informative Features in High Dimensions
Ge Shi, Fangyi Liu, Ziwen Kan +3
Post-hoc attribution is widely used to identify important model inputs, but evaluating whether these attributions identify truly informative features is difficult because real data…