Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Lopez, Diego Manzanas"'
Autor:
Robinette, Preston K., Lopez, Diego Manzanas, Serbinowska, Serena, Leach, Kevin, Johnson, Taylor T.
Malware, or software designed with harmful intent, is an ever-evolving threat that can have drastic effects on both individuals and institutions. Neural network malware classification systems are key tools for combating these threats but are vulnerab
Externí odkaz:
http://arxiv.org/abs/2404.05703
Autor:
Bogomolov, Sergiy, Johnson, Taylor T., Lopez, Diego Manzanas, Musau, Patrick, Stankaitis, Paulius
Publikováno v:
EPTCS 395, 2023, pp. 95-112
This paper presents an optimisation-based approach for an obstacle avoidance problem within an autonomous vehicle racing context. Our control regime leverages online reachability analysis and sensor data to compute the maximal safe traversable region
Externí odkaz:
http://arxiv.org/abs/2311.09781
Data-driven, neural network (NN) based anomaly detection and predictive maintenance are emerging research areas. NN-based analytics of time-series data offer valuable insights into past behaviors and estimates of critical parameters like remaining us
Externí odkaz:
http://arxiv.org/abs/2307.13907
Continuous deep learning models, referred to as Neural Ordinary Differential Equations (Neural ODEs), have received considerable attention over the last several years. Despite their burgeoning impact, there is a lack of formal analysis techniques for
Externí odkaz:
http://arxiv.org/abs/2207.06531
Autor:
Musau, Patrick, Hamilton, Nathaniel, Lopez, Diego Manzanas, Robinette, Preston, Johnson, Taylor T.
Recent advances in machine learning technologies and sensing have paved the way for the belief that safe, accessible, and convenient autonomous vehicles may be realized in the near future. Despite tremendous advances within this context, fundamental
Externí odkaz:
http://arxiv.org/abs/2205.01419
Autor:
Tran, Hoang-Dung, Yang, Xiaodong, Lopez, Diego Manzanas, Musau, Patrick, Nguyen, Luan Viet, Xiang, Weiming, Bak, Stanley, Johnson, Taylor T.
This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms
Externí odkaz:
http://arxiv.org/abs/2004.05519
Autor:
Xiang, Weiming, Musau, Patrick, Wild, Ayana A., Lopez, Diego Manzanas, Hamilton, Nathaniel, Yang, Xiaodong, Rosenfeld, Joel, Johnson, Taylor T.
This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intell
Externí odkaz:
http://arxiv.org/abs/1810.01989
Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the behaviors of neural network systems will be crucial for their applica
Externí odkaz:
http://arxiv.org/abs/1802.03557
Akademický článek
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Autor:
Tran, Hoang-Dung, Pal, Neelanjana, Lopez, Diego Manzanas, Musau, Patrick, Yang, Xiaodong, Nguyen, Luan Viet, Xiang, Weiming, Bak, Stanley, Johnson, Taylor T.
Publikováno v:
Formal Aspects of Computing; August 2021, Vol. 33 Issue: 4-5 p519-545, 27p
