Slicing, Chatting, and Refining: A Concept-Based Approach for Machine Learning Model Validation with ConceptSlicer-Reference-Cited by-同舟云学术

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Slicing, Chatting, and Refining: A Concept-Based Approach for Machine Learning Model Validation with ConceptSlicer

Published:2024-03-18 Issue: Volume:29 Page:274-287
ISSN:
Container-title:Proceedings of the 29th International Conference on Intelligent User Interfaces
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Author:

Zhang Xiaoyu¹^ORCID,Piazentin Ono Jorge H²^ORCID,He Wenbin²^ORCID,Gou Liang³^ORCID,Sachan Mrinmaya⁴^ORCID,Ma Kwan-Liu⁵^ORCID,Ren Liu²^ORCID

Affiliation:

1. ETH AI Center, ETH Zurich, Switzerland

2. BCAI, Robert Bosch Research and Technology Center, United States

3. Splunk Inc., United States

4. Computer Science, ETH Zurich, Switzerland

5. Computer Science Department, University of California at Davis, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3640543.3645163

Reference67 articles.

1. ESCAPE: Countering Systematic Errors from Machine’s Blind Spots via Interactive Visual Analysis

2. Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403 5, CSCW2 (2023).

3. Bridging the gap between ML solutions and their business requirements using feature interactions

4. Network Dissection: Quantifying Interpretability of Deep Visual Representations

5. Deep learning for magnification independent breast cancer histopathology image classification

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