CAT: Interpretable Concept-based Taylor Additive Models-Reference-Cited by-同舟云学术

CAT: Interpretable Concept-based Taylor Additive Models

Published:2024-08-24 Issue: Volume:35 Page:723-734
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Duong Viet¹^ORCID,Wu Qiong²^ORCID,Zhou Zhengyi²^ORCID,Zhao Hongjue³^ORCID,Luo Chenxiang¹^ORCID,Zavesky Eric⁴^ORCID,Yao Huaxiu⁵^ORCID,Shao Huajie¹^ORCID

Affiliation:

1. William & Mary, Williamsburg, VA, USA

2. AT&T Labs, Bedminster, NJ, USA

3. University of Illinois at Urbana-Champaign, Champaign, IL, USA

4. AT&T Labs, Austin, TX, USA

5. The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672020

Reference51 articles.

1. Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, and Geoffrey E Hinton. 2021. Neural additive models: Interpretable machine learning with neural nets. Advances in neural information processing systems, Vol. 34 (2021), 4699--4711.

2. I APACHE. 1985. APACHE II: A severity of disease classification system. (1985).

3. Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, Vol. 58 (2020), 82--115.

4. How to Standardize Regression Coefficients

5. Maxime Bucher, Stéphane Herbin, and Frédéric Jurie. 2019. Semantic bottleneck for computer vision tasks. In Computer Vision--ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2--6, 2018, Revised Selected Papers, Part II 14. Springer, 695--712.