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Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually.
Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations.
Generalized Approach to Matched Filtering using Neural Networks
Moreover, we show that the proposed neural network architecture can outperform matched filtering, both with or without knowledge of a prior on the parameter distribution.
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