We decompose the evidence lower bound to show the existence of a term measuring the
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …

Using established principles from Statistics and Information Theory, we show that invariance
to nuisance factors in a deep neural network is equivalent to information minimality of the …

Understanding predictive models, in terms of interpreting and identifying actionable insights,
is a challenging task. Often the importance of a feature in a model is only a rough estimate …

While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in
topic modeling, they often require detailed assumptions and careful specification of …

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread
to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths …

We propose a novel deep learning method for local self-supervised representation learning
that does not require labels nor end-to-end backpropagation but exploits the natural order in …

Within construction, we have become increasingly accustomed to relying on the benefits of
digital technologies, such as Building Information Modelling, to improve the performance …

Abstract Multi-View Representation Learning (MVRL) aims to discover a shared
representation of observations from different views with the complex underlying correlation …

Estimating mutual information from independent identically distributed samples drawn from
an unknown joint density function is a basic statistical problem of broad interest with …

Rework remains an ever-present reality in construction projects. How rework data is defined,
its format, location, and quantification contribute to the difficulty in managing its risks. This …