3 citations · 5 across the 8 of their papers we have counts for
8 papers
Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level
Ke Chen, Yifeng Wang, Hassan Almosapeeh +1
Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation…
Discovery of Disease Relationships via Transcriptomic Signature Analysis Powered by Agentic AI
Ke Chen, Haohan Wang
Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for…
Large Language Model-based Data Science Agent: A Survey
Ke Chen, Peiran Wang, Yaoning Yu +2
The rapid advancement of Large Language Models (LLMs) has driven novel applications across diverse domains, with LLM-based agents emerging as a crucial area of exploration. This su…
Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems
Ke Chen, Yufei Zhou, Xucheng Yu +2
Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Existing methods typically evaluate prompts…
IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts
Eric Xue, Ke Chen, Zeyi Huang +2
Large language model (LLM) agents have emerged as a promising solution to automate the workflow of machine learning, but most existing methods share a common limitation: they attem…
DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer's Early Diagnosis
Ke Chen, Yifeng Wang, Yufei Zhou +1
In the field of Alzheimer's disease diagnosis, segmentation and classification tasks are inherently interconnected. Sharing knowledge between models for these tasks can significant…
A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease Classification
Thomas Yu Chow Tam, Litian Liang, Ke Chen +2
Deep learning (DL) models have shown significant potential in Alzheimer's Disease (AD) classification. However, understanding and interpreting these models remains challenging, whi…
ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers
Yifeng Wang, Ke Chen, Haohan Wang
Automated diagnosis of Alzheimer Disease(AD) from brain imaging, such as magnetic resonance imaging (MRI), has become increasingly important and has attracted the community to cont…