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What is Interpretable? Using Machine Learning to Design Interpretable
  Decision-Support Systems

What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems

27 November 2018
O. Lahav
Nicholas Mastronarde
M. Schaar
ArXivPDFHTML

Papers citing "What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems"

6 / 6 papers shown
Title
Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Jathurshan Pradeepkumar
Mithunjha Anandakumar
Vinith Kugathasan
Dhinesh Suntharalingham
S. L. Kappel
A. D. Silva
Chamira U. S. Edussooriya
69
31
0
15 Aug 2022
Automatically Explaining Machine Learning Prediction Results: A
  Demonstration on Type 2 Diabetes Risk Prediction
Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction
G. Luo
25
85
0
06 Dec 2018
Instance-Level Explanations for Fraud Detection: A Case Study
Instance-Level Explanations for Fraud Detection: A Case Study
Dennis Collaris
L. M. Vink
J. V. Wijk
62
31
0
19 Jun 2018
A Survey Of Methods For Explaining Black Box Models
A Survey Of Methods For Explaining Black Box Models
Riccardo Guidotti
A. Monreale
Salvatore Ruggieri
Franco Turini
D. Pedreschi
F. Giannotti
XAI
124
3,957
0
06 Feb 2018
Learning Important Features Through Propagating Activation Differences
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
198
3,871
0
10 Apr 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
1.2K
16,976
0
16 Feb 2016
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