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Found 8 papers, 2 papers with code
Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification
Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS).
Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging
The resulting algorithm, fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations while retaining high reconstruction quality.
An Online Stochastic Kernel Machine for Robust Signal Classification
We present a novel variation of online kernel machines in which we exploit a consensus based optimization mechanism to guide the evolution of decision functions drawn from a reproducing kernel Hilbert space, which efficiently models the observed stationary process.
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization
Moreover, we are interested in sparse sensor selection using a marginalized weighted kernel approach to improve network resource efficiency by disabling less reliable sensors with minimal effect on classification performance. To achieve our goals, we develop a multi-sensor online kernel scalar quantization (MSOKSQ) learning strategy that operates on the sensor outputs at the fusion center.
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems
The first approach is an iterative algorithm that minimizes a regularized least squares objective function where the regularization is based on a compound Gaussian prior distribution.
Deep Regularized Compound Gaussian Network for Solving Linear Inverse Problems
Incorporating prior information into inverse problems, e. g. via maximum-a-posteriori estimation, is an important technique for facilitating robust inverse problem solutions.
On Generalization Bounds for Deep Compound Gaussian Neural Networks
In this paper, we develop novel generalization error bounds for a class of unrolled DNNs that are informed by a compound Gaussian prior.
Robust and tractable multidimensional exponential analysis
Motivated by a number of applications in signal processing, we study the following question.
