Detecting Anomalies within Time Series using Local Neural Transformations

1 code implementation • 8 Feb 2022 • Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, Maja Rudolph

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.

Anomaly Detection Epidemiology +3

Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications

no code implementations • 30 Jun 2020 • Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter

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.