Generalized Additive Models (GAMs) have recently experienced a resurgenc...
This work presents a novel deep-learning-based pipeline for the inverse
...
Countless signal processing applications include the reconstruction of
s...
Neural networks with random weights appear in a variety of machine learn...
This report is dedicated to a short motivation and description of our
co...
In the past five years, deep learning methods have become state-of-the-a...
Recent advances in quantized compressed sensing and high-dimensional
est...
This paper investigates total variation minimization in one spatial dime...
This work performs a non-asymptotic analysis of the (constrained) genera...
This paper investigates total variation minimization in one spatial dime...
We study the learning capacity of empirical risk minimization with regar...
This work theoretically studies the problem of estimating a structured
h...
In this paper, we study the challenge of feature selection based on a
re...
Background: High-throughput proteomics techniques, such as mass spectrom...