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Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic
In this work, we propose DIALOGUESTL, an interactive approach for learning correct and concise STL formulas from (often) ambiguous NL descriptions.
Mining Interpretable Spatio-temporal Logic Properties for Spatially Distributed Systems
There has been considerable interest in learning causal and logical properties of temporal data using logics such as Signal Temporal Logic (STL); however, there is limited work on discovering such relations on spatio-temporal data.
DiffRNN: Differential Verification of Recurrent Neural Networks
As the memory footprint and energy consumption of such components become a bottleneck, there is interest in compressing and optimizing such networks using a range of heuristic techniques.
Mining Environment Assumptions for Cyber-Physical System Models
We assume that the correctness of each component can be specified as a requirement satisfied by the output signals produced by the component, and that such an output guarantee is expressed in a real-time temporal logic such as Signal Temporal Logic (STL).
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data.
