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

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and
mortality rates. Early screening for CRC can improve cure rates and reduce mortality …

The ability to evaluate uncertainties in evolving data streams has become equally, if not
more, crucial than building a static predictor. For instance, during the pandemic, a model …

Traffic prediction facilitates various applications in the fields of smart vehicles and vehicular
communications, and the key of successfully and accurately forecasting urban traffic state is …

Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of
SRC carcinoma based on pathological images. Deep learning-based methods have …

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 …

Modeling and inferring the intricate stochastic variations of the manufacturing quality still
remain a significant challenge, especially when dealing with non-Euclidean functional data …

Deep transfer learning (DTL) is a fundamental method in the field of Intelligent Fault
Detection (IFD). It aims to mitigate the degradation of method performance that arises from …

Neural Process (NP) fully combines the advantages of neural network and Gaussian
Process (GP) to provide an efficient method for solving regression problems. Nonetheless …

Reinforcement learning (RL) may be the key to overcoming previ ous insurmountable
obstacles, leading to technological and scientific innovations. One such example where RL …