J Shen, X Zhen, Q Wang… - Advances in Neural …, 2023 - proceedings.neurips.cc
This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training setup. Specifically, we explore the potential of heterogeneous information …
Denoising diffusion models have proven to be a flexible and effective paradigm for generative modelling. Their recent extension to infinite dimensional Euclidean spaces has …
The standard approaches to neural network implementation yield powerful function approximation capabilities but are limited in their abilities to learn meta representations and …
Abstract Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty …
Spatial prediction tasks are challenging when observed samples are sparse and prediction samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …
Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the …
Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and …
P Brunzema, P Kruse, S Trimpe - 6th Annual Learning for …, 2024 - proceedings.mlr.press
Traditionally, first-principle models are used to monitor and control dynamical systems. However, modeling complex systems using first principles can be challenging. Learning the …
Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty. Recent state-of-the-art methods, however, leverage expensive …