280 citations · 2k across the 81 of their papers we have counts for
87 papers
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris +2
Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniqu…
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Kenza Tazi, Jihao Andreas Lin, Ross Viljoen +4
Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and…
Geometric Neural Diffusion Processes
Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson +3
Denoising diffusion models have proven to be a flexible and effective paradigm for generative modelling. Their recent extension to infinite dimensional Euclidean spaces has allowed…
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann +1
In this paper we explore few-shot imitation learning for control problems, which involves learning to imitate a target policy by accessing a limited set of offline rollouts. This s…
An Introduction to Transformers
Richard E. Turner
The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in na…
Autoregressive Conditional Neural Processes
Wessel P. Bruinsma, Stratis Markou, James Requiema +6
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum like…