Chaofan Chen
Chaofan Chen
Assistant Professor in Computer Science, the University of Maine
E-mailová adresa ověřena na: - Domovská stránka
This Looks Like That: Deep Learning for Interpretable Image Recognition
C Chen, O Li, C Tao, AJ Barnett, J Su, C Rudin
Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019
Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions
O Li, H Liu, C Chen, C Rudin
AAAI Conference on Artificial Intelligence (AAAI 2018), 2018
Interpretable machine learning: Fundamental principles and 10 grand challenges
C Rudin, C Chen, Z Chen, H Huang, L Semenova, C Zhong
Statistics Surveys 16, 1-85, 2022
An Interpretable Model with Globally Consistent Explanations for Credit Risk
C Chen, K Lin, C Rudin, Y Shaposhnik, S Wang, T Wang
NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial …, 2018
Interpretable Image Recognition with Hierarchical Prototypes
P Hase, C Chen, O Li, C Rudin
AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2019), 2019
An Optimization Approach to Learning Falling Rule Lists
C Chen, C Rudin
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018
A case-based interpretable deep learning model for classification of mass lesions in digital mammography
AJ Barnett, FR Schwartz, C Tao, C Chen, Y Ren, JY Lo, C Rudin
Nature Machine Intelligence 3 (12), 1061-1070, 2021
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
J Donnelly, AJ Barnett, C Chen
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations
C Chen, K Lin, C Rudin, Y Shaposhnik, S Wang, T Wang
Decision Support Systems 152, 113647, 2022
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning
AJ Barnett, FR Schwartz, C Tao, C Chen, Y Ren, JY Lo, C Rudin
arXiv preprint arXiv:2107.05605, 2021
Interpretability by Design: New Interpretable Machine Learning Models and Methods
C Chen
Duke University, 2020
A patient-informed approach to predict iodinated-contrast media enhancement in the liver
H Setiawan, C Chen, E Abadi, W Fu, D Marin, F Ria, E Samei
European Journal of Radiology, 110555, 2022
medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata
M Kimble, S Allers, K Campbell, C Chen, LM Jackson, BL King, ...
Bioinformatics, 2022
Interpretable deep learning models for better clinician-AI communication in clinical mammography
AJ Barnett, V Sharma, N Gajjar, J Fang, F Schwartz, C Chen, JY Lo, ...
Medical Imaging 2022: Image Perception, Observer Performance, and Technology …, 2022
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Články 1–14