From the 1 of 11 linked papers with an AI index.
11 papers
ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
Ruman Wang, Hangting Ye
The paper presents ScaFE, a system that uses a large language model to generate executable feature programs that measure visual scar attributes, enabling a lightweight Random Fores…
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
Ruman Wang, Hangting Ye
Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe…
TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning
Ruxue Shi, Yili Wang, Mengnan Du +3
Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples for new tasks is difficult. Ex…
Calibrating Tabular Anomaly Detection via Optimal Transport
Hangting Ye, He Zhao, Wei Fan +4
Tabular anomaly detection (TAD) remains challenging due to the heterogeneity of tabular data: features lack natural relationships, vary widely in distribution and scale, and exhibi…
LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
Hangting Ye, Jinmeng Li, He Zhao +4
Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance in real-world scenarios. While…
Deep Tabular Representation Corrector
Hangting Ye, Peng Wang, Wei Fan +4
Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc. The recent success of deep learning has fostered…