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Learning outside the Black-Box: The pursuit of interpretable models

Learning outside the Black-Box: The pursuit of interpretable models

17 November 2020
Jonathan Crabbé
Yao Zhang
W. Zame
M. Schaar
ArXivPDFHTML

Papers citing "Learning outside the Black-Box: The pursuit of interpretable models"

5 / 5 papers shown
Title
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in
  Healthcare with Automated Machine Learning
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
F. Imrie
B. Cebere
E. McKinney
M. Schaar
68
20
0
21 Oct 2022
D-CIPHER: Discovery of Closed-form Partial Differential Equations
D-CIPHER: Discovery of Closed-form Partial Differential Equations
Krzysztof Kacprzyk
Zhaozhi Qian
M. Schaar
AI4CE
30
1
0
21 Jun 2022
Label-Free Explainability for Unsupervised Models
Label-Free Explainability for Unsupervised Models
Jonathan Crabbé
M. Schaar
FAtt
MILM
24
22
0
03 Mar 2022
Precise characterization of the prior predictive distribution of deep
  ReLU networks
Precise characterization of the prior predictive distribution of deep ReLU networks
Lorenzo Noci
Gregor Bachmann
Kevin Roth
Sebastian Nowozin
Thomas Hofmann
BDL
UQCV
29
32
0
11 Jun 2021
On the Computational Intelligibility of Boolean Classifiers
On the Computational Intelligibility of Boolean Classifiers
Gilles Audemard
S. Bellart
Louenas Bounia
F. Koriche
Jean-Marie Lagniez
Pierre Marquis
24
56
0
13 Apr 2021
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