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This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously.
The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.
Our in-lab study shows that GesturePod achieves 92% gesture recognition accuracy and can help perform common smartphone tasks faster.
SOTA for Gesture Recognition on GesturePod
FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.
We propose a method, EMI-RNN, that exploits these observations by using a multiple instance learning formulation along with an early prediction technique to learn a model that achieves better accuracy compared to baseline models, while simultaneously reducing computation by a large fraction.
Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series.
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
#2 best model for Time Series Classification on Physionet 2017 Atrial Fibrillation
We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC.
We propose a simple but strong baseline for time series classification from scratch with deep neural networks.