26 citations · 64 across the 11 of their papers we have counts for
12 papers
Exposing Previously Undetectable Faults in Deep Neural Networks
Isaac Dunn, Hadrien Pouget, Daniel Kroening +1
Existing methods for testing DNNs solve the oracle problem by constraining the raw features (e.g. image pixel values) to be within a small distance of a dataset example for which t…
Hardware/Software Co-verification Using Path-based Symbolic Execution
Rajdeep Mukherjee, Saurabh Joshi, John O'Leary +2
Conventional tools for formal hardware/software co-verification use bounded model checking techniques to construct a single monolithic propositional formula. Formulas generated in…
Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities
Isaac Dunn, Laura Hanu, Hadrien Pouget +2
We cannot guarantee that training datasets are representative of the distribution of inputs that will be encountered during deployment. So we must have confidence that our models d…
Learning Concise Models from Long Execution Traces
Natasha Yogananda Jeppu, Tom Melham, Daniel Kroening +1
Abstract models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting usef…
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate +2
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress…
CREST: Hardware Formal Verification with ANSI-C Reference Specifications
Andreas Tiemeyer, Tom Melham, Daniel Kroening +1
This paper presents CREST, a prototype front-end tool intended as an add-on to commercial EDA formal verifcation environments. CREST is an adaptation of the CBMC bounded model chec…