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Natively Interpretable Machine Learning and Artificial Intelligence:
  Preliminary Results and Future Directions

Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions

2 January 2019
Christopher J. Hazard
Christopher Fusting
Michael Resnick
Michael Auerbach
M. Meehan
Valeri Korobov
ArXivPDFHTML

Papers citing "Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions"

2 / 2 papers shown
Title
Evaluating and Aggregating Feature-based Model Explanations
Evaluating and Aggregating Feature-based Model Explanations
Umang Bhatt
Adrian Weller
J. M. F. Moura
XAI
35
218
0
01 May 2020
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic
  Analysis
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis
Andrea Morichetta
P. Casas
Marco Mellia
12
55
0
03 Mar 2020
1