Summary of Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence, by Joseph Paillard et al.
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidenceby Joseph Paillard, Angel Reyero Lobo, Vitaliy…
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidenceby Joseph Paillard, Angel Reyero Lobo, Vitaliy…
IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Modelsby Zhihao Yu, Yujie…
Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysisby Yuxiang Wei, Anees Abrol, Vince CalhounFirst submitted to…
Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classificationby Ziwen Kan, Shahbaz Rezaei,…
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacksby Zhibo Jin, Jiayu Zhang,…
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banksby Woojin Shin, Donghwa Kang, Daejin…
MultiMed: Massively Multimodal and Multitask Medical Understandingby Shentong Mo, Paul Pu LiangFirst submitted to arxiv…
Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for…
Segment Anything Model for Grain Characterization in Hard Drive Designby Kai Nichols, Matthew Hauwiller, Nicholas…
SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Mergingby Mohammadreza Pourreza,…