This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

We explore a class of end-to-end learnable models wherein data processing nodes (or
network layers) are defined in terms of desired behavior rather than an explicit forward …

Some recent works in machine learning and computer vision involve the solution of a bi-
level optimization problem. Here the solution of a parameterized lower-level problem binds …

Encoding models provide a powerful framework to identify the information represented in
brain recordings. In this framework, a stimulus representation is expressed within a feature …

We review some recent learning approaches in variational imaging based on bilevel
optimization and emphasize the importance of their treatment in function space. The paper …

We introduce a new model for representation learning and classification of video
sequences. Our model is based on a convolutional neural network coupled with a novel …

Fitting a machine learning model often requires presetting parameter values
(hyperparameters) that control how an algorithm learns from the data. Selecting an optimal …

A weighted total variation model with a spatially varying regularization weight is considered.
Existence of a solution is shown, and the associated Fenchel predual problem is derived …

We analyse a variational regularisation problem for mixed noise removal that has been
recently proposed in Calatroni et al (2017 SIAM J. Imaging Sci. 10 1196–233). The data …

This article looks at predicting the price of platinum, along with 12 other commodity prices,
using both time series and machine learning models. Platinum, characterised by its rarity …