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Found 6 papers, 1 papers with code
Detecting Anomalies within Time Series using Local Neural Transformations
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications
Additionally, we show how specific intervals of fuel injection quantities can be targeted to maximize robustness for certain ranges, allowing us to train a virtual sensor for fuel injection which is provably guaranteed to have at most 10. 69% relative error under noise while maintaining 3% relative error on non-adversarial data within normalized fuel injection ranges of 0. 6 to 1. 0.
Active Inference for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in the last decades.
Active Exploration for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years.
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning
Symbolic Regression (SR) allows for the discovery of scientific equations from data.
What Matters for Active Texture Recognition With Vision-Based Tactile Sensors
By evaluating our method on a previously published Active Clothing Perception Dataset and on a real robotic system, we establish that the choice of the active exploration strategy has only a minor influence on the recognition accuracy, whereas data augmentation and dropout rate play a significantly larger role.
