Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Brendon G. Anderson"'
Publikováno v:
2022 IEEE 61st Conference on Decision and Control (CDC).
Publikováno v:
CDC
In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data. Such certification is imperative for the application of neural networks in safety-critical decision-making and control sys
Autor:
Brendon G. Anderson, Somayeh Sojoudi
Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings. Most certification methods in the literature are designed for adversarial or worst-case inputs, but researchers have rece
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e50c20eb1cdaeb3240abbbd5570144e5
Autor:
Marissa Gee, Eva Loeser, Swagata Biswas, Fei Ren, Matt Haberland, Brendon G. Anderson, Andrea L. Bertozzi, Olga Turanova
Publikováno v:
Informatics in Control, Automation and Robotics ISBN: 9783030319922
ICINCO (Selected Papers)
ICINCO (Selected Papers)
In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::062c8f2e6061e451112021e70d3a9b36
https://doi.org/10.1007/978-3-030-31993-9_13
https://doi.org/10.1007/978-3-030-31993-9_13
Autor:
Somayeh Sojoudi, Brendon G. Anderson
Publikováno v:
Allerton
In this paper, we consider the problem of unsupervised video object segmentation via background subtraction. Specifically, we pose the nonsemantic extraction of a video's moving objects as a nonconvex optimization problem via a sum of sparse and low-
Autor:
Olga Turanova, Marissa Gee, Brendon G. Anderson, Eva Loeser, Andrea L. Bertozzi, Matt Haberland, Fei Ren, Swagata Biswas
Publikováno v:
ICINCO (2)
Anderson, BG; Loeser, E; Gee, M; Ren, F; Biswas, S; Turanova, O; et al.(2018). Quantitative Assessment of Robotic Swarm Coverage. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO)---Volume 2, 2018, pp. 91--101. doi: 10.5220/0006844601010111. UCLA: Retrieved from: http://www.escholarship.org/uc/item/7rz9z3w9
Anderson, BG; Loeser, E; Gee, M; Ren, F; Biswas, S; Turanova, O; et al.(2018). Quantitative Assessment of Robotic Swarm Coverage. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO)---Volume 2, 2018, pp. 91--101. doi: 10.5220/0006844601010111. UCLA: Retrieved from: http://www.escholarship.org/uc/item/7rz9z3w9
This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numerical methods, it overcomes the shortcomings of a common strategy based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35d9c7dc5d42518f069d145994427069
https://escholarship.org/uc/item/7rz9z3w9
https://escholarship.org/uc/item/7rz9z3w9