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Found 3 papers, 1 papers with code
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection
In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS, learning heterogeneous structure information from a mass of unlabeled time-series data to improve the accuracy of anomaly detection, and using attention coefficient to provide an explanation for the detected anomalies.
Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures: A Machine Learning Based Approach
This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures.
Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach
In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given application, targeting the Intel XeonPhi architecture.
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