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Sample-Specific Output Constraints for Neural Networks

Sample-Specific Output Constraints for Neural Networks

23 March 2020
Mathis Brosowsky
Olaf Dünkel
Daniel Slieter
Marius Zöllner
    AILaw
    PINN
ArXivPDFHTML

Papers citing "Sample-Specific Output Constraints for Neural Networks"

3 / 3 papers shown
Title
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
219
1,818
0
03 Feb 2017
Safety Verification of Deep Neural Networks
Safety Verification of Deep Neural Networks
Xiaowei Huang
M. Kwiatkowska
Sen Wang
Min Wu
AAML
178
883
0
21 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015
1