Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works

1 code implementation • 19 Apr 2024 • Xinfeng Yuan, Siyu Yuan, Yuhan Cui, Tianhe Lin, Xintao Wang, Rui Xu, Jiangjie Chen, Deqing Yang

These results strongly validate the character understanding capability of LLMs.

A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

no code implementations • 18 Mar 2023 • Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang shen, Jie zhou, Siming Chen, Tao Gui, Qi Zhang, Xuanjing Huang

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.

Natural Language Understanding

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

1 code implementation • 25 Jan 2023 • Zhijian Yang, Junhao Wen, Ahmed Abdulkadir, Yuhan Cui, Guray Erus, Elizabeth Mamourian, Randa Melhem, Dhivya Srinivasan, Sindhuja T. Govindarajan, Jiong Chen, Mohamad Habes, Colin L. Masters, Paul Maruff, Jurgen Fripp, Luigi Ferrucci, Marilyn S. Albert, Sterling C. Johnson, John C. Morris, Pamela Lamontagne, Daniel S. Marcus, Tammie L. S. Benzinger, David A. Wolk, Li Shen, Jingxuan Bao, Susan M. Resnick, Haochang Shou, Ilya M. Nasrallah, Christos Davatzikos

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.

Deep Clustering

STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison

1 code implementation • 23 Jun 2022 • Ryan J. Urbanowicz, Robert Zhang, Yuhan Cui, Pranshu Suri

Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations.

Binary Classification Feature Importance +3

A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments

2 code implementations • 28 Aug 2020 • Ryan J. Urbanowicz, Pranshu Suri, Yuhan Cui, Jason H. Moore, Karen Ruth, Rachael Stolzenberg-Solomon, Shannon M. Lynch

Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex associations.

Binary Classification feature selection +1