Abstract Gaussian Processes (GPs) provide an extremely powerful mechanism to model a
variety of problems but incur an O (N 3) complexity in the number of data samples. Common …

Spatiotemporal patterns of large-scale spiking and field potentials of the rodent
hippocampus encode spatial representations during maze runs, immobility, and sleep. Here …

Specifying utility functions is a key step towards applying the discrete choice framework for
understanding the behaviour processes that govern user choices. However, identifying the …

Much of our real-life decision making is bounded by uncertain information, limitations in
cognitive resources, and a lack of time to allocate to the decision process. It is thought that …

Detecting P300 slow-cortical ERPs poses a considerable challenge in signal processing
due to the complex and non-stationary characteristics of a single-trial EEG signal. EEG …

This work presents a new scalable automatic Bayesian Lasso methodology with variational
inference for non-parametric splines regression that can capture the non-linear relationship …

Large-scale fluorescence calcium imaging methods have become widely adopted for
studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the …

To increase statistical power to identify genes associated with complex traits, a number of
transcriptome-wide association study (TWAS) methods have been proposed using gene …

The ability to generate accurate terrain models is of key importance in a wide variety of
robotics tasks, ranging from path planning and trajectory optimization to environment …

Machine learning (ML) is increasingly applied to predict adverse postoperative outcomes in
cardiac surgery. Commonly used ML models fail to translate to clinical practice due to …