5 research outputs found
Structure-preserving deep learning
Over the past few years, deep learning has risen to the foreground as a topic
of massive interest, mainly as a result of successes obtained in solving
large-scale image processing tasks. There are multiple challenging mathematical
problems involved in applying deep learning: most deep learning methods require
the solution of hard optimisation problems, and a good understanding of the
tradeoff between computational effort, amount of data and model complexity is
required to successfully design a deep learning approach for a given problem. A
large amount of progress made in deep learning has been based on heuristic
explorations, but there is a growing effort to mathematically understand the
structure in existing deep learning methods and to systematically design new
deep learning methods to preserve certain types of structure in deep learning.
In this article, we review a number of these directions: some deep neural
networks can be understood as discretisations of dynamical systems, neural
networks can be designed to have desirable properties such as invertibility or
group equivariance, and new algorithmic frameworks based on conformal
Hamiltonian systems and Riemannian manifolds to solve the optimisation problems
have been proposed. We conclude our review of each of these topics by
discussing some open problems that we consider to be interesting directions for
future research
Seasonal and event dynamics of spatial soil moisture patterns at the small catchment scale
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection
and prognostication of COVID-19 from standard-of-care chest radiographs (CXR)
and computed tomography (CT) images. Many articles have been published in 2020
describing new machine learning-based models for both of these tasks, but it is
unclear which are of potential clinical utility. In this systematic review, we
search EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for
published papers and preprints uploaded from January 1, 2020 to October 3, 2020
which describe new machine learning models for the diagnosis or prognosis of
COVID-19 from CXR or CT images. Our search identified 2,212 studies, of which
415 were included after initial screening and, after quality screening, 61
studies were included in this systematic review. Our review finds that none of
the models identified are of potential clinical use due to methodological flaws
and/or underlying biases. This is a major weakness, given the urgency with
which validated COVID-19 models are needed. To address this, we give many
recommendations which, if followed, will solve these issues and lead to higher
quality model development and well documented manuscripts