7 citations · 11 across the 6 of their papers we have counts for
9 papers
Backdoor Mitigation in Deep Neural Networks via Strategic Retraining
Akshay Dhonthi, Ernst Moritz Hahn, Vahid Hashemi
Deep Neural Networks (DNN) are becoming increasingly more important in assisted and automated driving. Using such entities which are obtained using machine learning is inevitable:…
SMC4PEP: Stochastic Model Checking of Product Engineering Processes
Hassan Hage, Emmanouil Seferis, Vahid Hashemi +1
Product Engineering Processes (PEPs) are used for describing complex product developments in big enterprises such as automotive and avionics industries. The Business Process Model…
Towards a Systems Engineering based Automotive Product Engineering Process
Hassan Hage, Vahid Hashemi, Frank Mantwill
Deficit and redundancies in existing automotive product development hinder a systems engineering based development. In this paper we discuss a methodical procedure to eliminate def…
DeepAbstract: Neural Network Abstraction for Accelerating Verification
Pranav Ashok, Vahid Hashemi, Jan Křetínský +1
While abstraction is a classic tool of verification to scale it up, it is not used very often for verifying neural networks. However, it can help with the still open task of scalin…
Towards Safety Verification of Direct Perception Neural Networks
Chih-Hong Cheng, Chung-Hao Huang, Thomas Brunner +1
We study the problem of safety verification of direct perception neural networks, where camera images are used as inputs to produce high-level features for autonomous vehicles to m…
Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Dimitri Scheftelowitsch, Peter Buchholz, Vahid Hashemi +1
Markov decision processes (MDPs) are a popular model for performance analysis and optimization of stochastic systems. The parameters of stochastic behavior of MDPs are estimates fr…
Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes
Ernst Moritz Hahn, Vahid Hashemi, Holger Hermanns +2
Interval Markov decision processes (IMDPs) generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic sy…
Compositional Reasoning for Interval Markov Decision Processes
Vahid Hashemi, Holger Hermanns, Andrea Turrini
Model checking probabilistic CTL properties of Markov decision processes with convex uncertainties has been recently investigated by Puggelli et al. Such model checking algorithms…
Probabilistic Bisimulations for PCTL Model Checking of Interval MDPs
Vahid Hashemi, Hassan Hatefi, Jan Krčál
Verification of PCTL properties of MDPs with convex uncertainties has been investigated recently by Puggelli et al. However, model checking algorithms typically suffer from state s…