2 citations · 2 across the 1 of their papers we have counts for
6 papers
Engineering problems in machine learning systems
Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles…
Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems
Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Qual…
Runtime Monitoring Neuron Activation Patterns
Chih-Hong Cheng, Georg Nührenberg, Hirotoshi Yasuoka
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runt…
Towards Dependability Metrics for Neural Networks
Chih-Hong Cheng, Georg Nührenberg, Chung-Hao Huang +2
Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality. An overarching challenge is to identify best safety engineering practices for…
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems
Chih-Hong Cheng, Chung-Hao Huang, Hirotoshi Yasuoka
Systematically testing models learned from neural networks remains a crucial unsolved barrier to successfully justify safety for autonomous vehicles engineered using data-driven ap…
Quantitative Information Flow as Safety and Liveness Hyperproperties
Hirotoshi Yasuoka, Tachio Terauchi
We employ Clarkson and Schneider's "hyperproperties" to classify various verification problems of quantitative information flow. The results of this paper unify and extend the prev…