A wide variety of activation functions have been proposed for neural networks. The Rectified
Linear Unit (ReLU) is especially popular today. There are many practical reasons that …

Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches
to perform early bearing damage identification in induction motors. This approach is …

We focus on the generalized-interpolation problem. There, one reconstructs continuous-
domain signals that honor discrete data constraints. This problem is infinite-dimensional and …

We introduce a general framework for the reconstruction of periodic multivariate functions
from finitely many and possibly noisy linear measurements. The reconstruction task is …

We study the problem of one-dimensional regression of data points with total-variation (TV)
regularization (in the sense of measures) on the second derivative, which is known to …

Regression is one of the core problems tackled in supervised learning. Neural networks with
rectified linear units generate continuous and piecewise-linear (CPWL) mappings and are …

We formulate as an inverse problem the construction of sparse parametric continuous curve
models that fit a sequence of contour points. Our prior is incorporated as a regularization …

The Lizorkin space is well suited to the study of operators like fractional Laplacians and the
Radon transform. In this paper, we show that the space is unfortunately not complemented in …

We study one-dimensional continuous-domain inverse problems with multiple generalized
total-variation regularization, which involves the joint use of several regularization operators …

We study the problem of interpolating one-dimensional data with total variation
regularization on the second derivative, which is known to promote piecewise-linear …