In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)” and an improved model training method to break the bottleneck of neural network …
This paper provides a comprehensive survey of techniques for testing machine learning systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
Y Tian, K Pei, S Jana, B Ray - … of the 40th international conference on …, 2018 - dl.acm.org
Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN- driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any …
Abstract Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The …
Recent advances in Language Model (LM) agents and tool use, exemplified by applications like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks-such …
Y Ma, C Sun, J Chen, D Cao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Verification and validation (V&V) hold a significant position in the research and development of automated vehicles (AVs). Current literature indicates that different V&V techniques have …
This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …
A Gambi, M Mueller, G Fraser - Proceedings of the 28th ACM SIGSOFT …, 2019 - dl.acm.org
Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving car software in real traffic is not only expensive but also dangerous, and has already caused …
RB Abdessalem, S Nejati, LC Briand… - Proceedings of the 40th …, 2018 - dl.acm.org
Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and …