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Found 7 papers, 1 papers with code
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds
In this work, we study TpopT (TemPlate OPTimization) as an alternative scalable framework for detecting low-dimensional families of signals which maintains high interpretability.
Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks
Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy.
Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization
Achieving invariance to nuisance transformations is a fundamental challenge in the construction of robust and reliable vision systems.
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.
Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery
We show that a single, universal choice of the regularization parameter suffices to achieve reconstruction error proportional to the (a priori unknown) noise level.
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.
