31 citations · 242 across the 55 of their papers we have counts for
4 papers · 2 filters
Mungojerrie: Reinforcement Learning of Linear-Time Objectives
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
Reinforcement learning synthesizes controllers without prior knowledge of the system. At each timestep, a reward is given. The controllers optimize the discounted sum of these rewa…
Model-free Reinforcement Learning for Branching Markov Decision Processes
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state…
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
Xingyu Zhao, Wei Huang, Alec Banks +4
The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications. While it shows great potential to provide transformational capabilities, DL al…
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features
Nicolas Berthier, Amany Alshareef, James Sharp +2
Intensive research has been conducted on the verification and validation of deep neural networks (DNNs), aiming to understand if, and how, DNNs can be applied to safety critical ap…