Time series deals with sequential data where the data is indexed (ordered) by a time dimension.
( Image credit: Autoregressive CNNs for Asynchronous Time Series )
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We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
SOTA for Time Series on Amazon
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.
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
SOTA for Time Series on Bitcoin-Alpha
Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data.
#3 best model for Anomaly Detection on Numenta Anomaly Benchmark
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.
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.
SOTA for Graph Classification on ENZYMES