6 citations · 10 across the 7 of their papers we have counts for
7 papers
CAi Copilot: Reducing Operational Workload in Molecular Design through Intent-Driven Agentic Workflows
Zhu Wang, Jiangyu Chen, Yingjun Shang +4
Early-stage molecular design is an iterative process, not just a task of generating molecules. Researchers turn broad goals into design strategies, refine candidates, assess many p…
Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform
Jinliang Yu, Mingduo Xie, Yue Wang +3
Clinical cystoscopy, the current standard for bladder cancer diagnosis, suffers from significant reliance on physician expertise, leading to variability and subjectivity in diagnos…
Foundation Model in Biomedicine
Xiangrui Liu, Yuanyuan Zhang, Qianyu Shang +14
Foundation models, first introduced in 2021, refer to large-scale pretrained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive…
Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
Zhiyuan Wang, Chunlin Feng, Christopher Poon +5
Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) er…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
BioMamba: Domain-Adaptive Biomedical Language Models
Ling Yue, Mingzhi Zhu, Sixue Xing +8
Background. Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-based models, this trade-off has n…
Uncertainty Quantification on Clinical Trial Outcome Prediction
Tianyi Chen, Yingzhou Lu, Nan Hao +4
The importance of uncertainty quantification is increasingly recognized in the diverse field of machine learning. Accurately assessing model prediction uncertainty can help provide…