Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Papusha, Ivan"'
Autor:
Kouskoulas, Yanni, Machado, T. J., Genin, Daniel, Schmidt, Aurora, Papusha, Ivan, Brulé, Joshua
Publikováno v:
International Journal on Software Tools for Technology Transfer, 2022
We present an approach to analyzing the safety of asynchronous, independent, non-deterministic, turn-to-bearing horizontal maneuvers for two vehicles. Future turn rates, final bearings, and continuously varying ground speeds throughout the encounter
Externí odkaz:
http://arxiv.org/abs/2205.04833
There is great interest in using formal methods to guarantee the reliability of deep neural networks. However, these techniques may also be used to implant carefully selected input-output pairs. We present initial results on a novel technique for usi
Externí odkaz:
http://arxiv.org/abs/2008.01204
Autor:
Papusha, Ivan Igorevych
Recent technological advances have opened the door to a wide variety of dynamic control applications, which are enabled by increasing computational power in ever smaller devices. These advances are backed by reliable optimization algorithms that allo
Externí odkaz:
https://thesis.library.caltech.edu/9831/1/ipthesis.pdf
We introduce a novel architecture and computational framework for formal, automated analysis of systems with a broad set of nonlinearities in the feedback loop, such as neural networks, vision controllers, switched systems, and even simple programs.
Externí odkaz:
http://arxiv.org/abs/1805.00164
Autor:
Cubuktepe, Murat, Jansen, Nils, Junges, Sebastian, Katoen, Joost-Pieter, Papusha, Ivan, Poonawala, Hasan A., Topcu, Ufuk
Multi-objective verification problems of parametric Markov decision processes under optimality criteria can be naturally expressed as nonlinear programs. We observe that many of these computationally demanding problems belong to the subclass of signo
Externí odkaz:
http://arxiv.org/abs/1702.00063
This work proposes a method for solving linear stochastic optimal control (SOC) problems using sum of squares and semidefinite programming. Previous work had used polynomial optimization to approximate the value function, requiring a high polynomial
Externí odkaz:
http://arxiv.org/abs/1409.5986
Akademický článek
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Autor:
Kelly, Michael A., Dong Wu, Goldberg, Arnold, Papusha, Ivan, Wilson, John, Carr, James, Boldt, John, Greenberg, Jacob, Morgan, Frank, Sam Yee, Heidinger, Andrew, Mehr, Lauren
Publikováno v:
Proceedings of SPIE; 7/23/2018, Vol. 10776, p1-10, 10p
Publikováno v:
2016 IEEE 55th Conference on Decision & Control (CDC); 2016, p3306-3311, 6p
Publikováno v:
2016 IEEE 55th Conference on Decision & Control (CDC); 2016, p434-440, 7p