7 citations · 11 across the 5 of their papers we have counts for
6 papers
On Inverse Problems, Parameter Estimation, and Domain Generalization
Deborah Pereg
Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of machine learning methods, inversio…
One-Shot Image Restoration
Deborah Pereg
Image restoration, or inverse problems in image processing, has long been an extensively studied topic. In recent years supervised learning approaches have become a popular strateg…
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction
Deborah Pereg
Speckle noise has long been an extensively studied problem in medical imaging. In recent years, there have been significant advances in leveraging deep learning methods for noise r…
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets
Deborah Pereg, Martin Villiger, Brett Bouma +1
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset. The learner is…
Convolutional Sparse Coding Fast Approximation with Application to Seismic Reflectivity Estimation
Deborah Pereg, Israel Cohen, Anthony A. Vassiliou
In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks. Similarly, ne…
Blind Deconvolution via Maximum Kurtosis Adaptive Filtering
Deborah Pereg, Doron Benzvi
In this paper, we present an algorithm for identifying a parametrically described destructive unknown system based on a non-gaussianity measure. It is known that under certain cond…