2017
Learning Time Series Detection Models from Temporally Imprecise Labels
AISTATS 2017poster
In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence…