Recently, deep learning has unlocked unprecedented success in various domains,
especially using images, text, and speech. However, deep learning is only beneficial if the …

Neural networks models for NLP are typically implemented without the explicit encoding of
language rules and yet they are able to break one performance record after another. This …

To study a core component of human intelligence—our ability to combine the meaning of
words—neuroscientists have looked to linguistics. However, linguistic theories are …

While studying semantics in the brain, neuroscientists use two approaches. One is to identify
areas that are correlated with semantic processing load. Another is to find areas that are …

In many practical applications of multiple testing, there are natural ways to partition the
hypotheses into groups by using the structural, spatial or temporal relatedness of the …

An increasingly important data source for the development of clinical risk prediction models
is electronic health records (EHRs). One of their key advantages is that they contain data on …

While studying semantics in the brain, neuroscientists use two approaches. One is to identify
areas that are correlated with semantic processing load. Another is to find areas that are …

In neuroimaging data analysis, Gaussian graphical models are often used to model
statistical dependencies across spatially remote brain regions known as functional …

Small area estimation is concerned with methodology for estimating population parameters
associated with a geographic area defined by a cross‐classification that may also include …

Brain encoding with neuroimaging data is an established analysis aimed at predicting
human brain activity directly from complex stimuli features such as movie frames. Typically …