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Infinite Time Horizon Safety of Bayesian Neural Networks

Infinite Time Horizon Safety of Bayesian Neural Networks

4 November 2021
Mathias Lechner
Dorde Zikelic
K. Chatterjee
T. Henzinger
ArXivPDFHTML

Papers citing "Infinite Time Horizon Safety of Bayesian Neural Networks"

5 / 5 papers shown
Title
Bayesian inference for data-efficient, explainable, and safe robotic
  motion planning: A review
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Chengmin Zhou
Chao Wang
Haseeb Hassan
H. Shah
Bingding Huang
Pasi Fränti
3DV
43
3
0
16 Jul 2023
Learning Control Policies for Stochastic Systems with Reach-avoid
  Guarantees
Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
Dorde Zikelic
Mathias Lechner
T. Henzinger
K. Chatterjee
29
22
0
11 Oct 2022
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot
  Learning
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
Mathias Lechner
Alexander Amini
Daniela Rus
T. Henzinger
AAML
34
10
0
15 Apr 2022
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
251
1,842
0
03 Feb 2017
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
287
9,167
0
06 Jun 2015
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