8 citations · 37 across the 26 of their papers we have counts for
5 papers · 1 filter
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System
Guy Amir, Ziv Freund, Guy Katz +2
In this short paper, we present our ongoing work on the veriFIRE project -- a collaboration between industry and academia, aimed at using verification for increasing the reliabilit…
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming
Davide Corsi, Raz Yerushalmi, Guy Amir +3
Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications. A natural consequence is the adoption of this paradigm for safety…
Verifying Learning-Based Robotic Navigation Systems
Guy Amir, Davide Corsi, Raz Yerushalmi +4
Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies a…
Scenario-Assisted Deep Reinforcement Learning
Raz Yerushalmi, Guy Amir, Achiya Elyasaf +3
Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that th…
Verification-Aided Deep Ensemble Selection
Guy Amir, Tom Zelazny, Guy Katz +1
Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex tasks. However, as highlighted by many recent studies, even an imperceptible per…