Recent work has shown how to embed differentiable optimization problems (that is,
problems whose solutions can be backpropagated through) as layers within deep learning …

This paper presents a novel design of a Linear Parameter Varying (LPV) controller based-on
the polytopic approach, for the path-following system of an automated vehicle. The …

Abstract Domain-specific modeling priors and specialized components are becoming
increasingly important to the machine learning field. These components integrate …

Popular methods to deal with computation become strenuous due to the optimization
demands that develop more complex nowadays. This research aims to propose a new …

Cellulose, hemicellulose, and lignin molecules in poplar wood are interwoven to form a
dense network-like structure, which prevents their degradation into oligomers for the …

We present two model-based neural network architectures purposed for sporadic user
detection and channel estimation in massive machine-type communications. In the scenario …

A convex optimization model predicts an output from an input by solving a convex
optimization problem. The class of convex optimization models is large, and includes as …

Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer
design. However, there are two main challenges when applying these layers to computer …

This paper considers the problem of fitting the parameters in a Kalman smoother to data. We
formulate the Kalman smoothing problem with missing measurements as a constrained least …

The accurate identification of the parameters of fractional-order systems is still a challenging
problem. The purpose of this paper is to establish a fractional hysteresis model based on a …