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Understanding Learned Models by Identifying Important Features at the
  Right Resolution
v1v2 (latest)

Understanding Learned Models by Identifying Important Features at the Right Resolution

18 November 2018
Kyubin Lee
Akshay Sood
M. Craven
ArXiv (abs)PDFHTML

Papers citing "Understanding Learned Models by Identifying Important Features at the Right Resolution"

4 / 4 papers shown
Title
Variable Importance in High-Dimensional Settings Requires Grouping
Variable Importance in High-Dimensional Settings Requires Grouping
Ahmad Chamma
Bertrand Thirion
Denis A. Engemann
68
6
0
18 Dec 2023
Feature Importance Explanations for Temporal Black-Box Models
Feature Importance Explanations for Temporal Black-Box Models
Akshay Sood
M. Craven
FAttOOD
123
16
0
23 Feb 2021
Machine Guides, Human Supervises: Interactive Learning with Global
  Explanations
Machine Guides, Human Supervises: Interactive Learning with Global Explanations
Teodora Popordanoska
Mohit Kumar
Stefano Teso
115
21
0
21 Sep 2020
Boxer: Interactive Comparison of Classifier Results
Boxer: Interactive Comparison of Classifier Results
Michael Gleicher
Aditya Barve
Xinyi Yu
Florian Heimerl
VLMHAI
83
40
0
16 Apr 2020
1