The neural process family: Survey, applications and perspectives
The standard approaches to neural network implementation yield powerful function
approximation capabilities but are limited in their abilities to learn meta representations and …
approximation capabilities but are limited in their abilities to learn meta representations and …
Uncertainty estimation with neural processes for meta-continual learning
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 …
more, crucial than building a static predictor. For instance, during the pandemic, a model …
Spatial multi-attention conditional neural processes
LL Bao, JS Zhang, CX Zhang - Neural Networks, 2024 - Elsevier
Spatial prediction tasks are challenging when observed samples are sparse and prediction
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …