The standard approaches to neural network implementation yield powerful function
approximation capabilities but are limited in their abilities to learn meta representations and …

Spatial prediction tasks are challenging when observed samples are sparse and prediction
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …

With the exploration of outer space, the number of space targets has increased dramatically,
while the pressures of space situational awareness have also increased. Among them …

Neural processes are a family of models which use neural networks to directly parametrise a
map from data sets to predictions. Directly parametrising this map enables the use of …

Most few-shot learning methods employ either adaptive approaches or parameter
amortization techniques. However, their reliance on pre-trained models presents a …

Data assimilation is a vital component in modern global medium-range weather forecasting
systems to obtain the best estimation of the atmospheric state by combining the short-term …

Convolutional deep sets is a neural network architecture that can model stationary
stochastic processes. This architecture uses the kernel smoother and deep convolutional …

Convolutional deep sets are the architecture of a deep neural network (DNN) that can model
stationary stochastic process. This architecture uses the kernel smoother and the DNN to …

Traffic forecasting is essential for optimising intelligent transport systems (ITS), directly
impacting traffic congestion, accident reduction, and transportation efficiency. With the …