65 citations · 107 across the 16 of their papers we have counts for
18 papers
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang +1
Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel parameterizations must…
Valid Error Bars for Neural Weather Models using Conformal Prediction
Vignesh Gopakumar, Joel Oskarrson, Ander Gray +5
Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do…
Thin and Deep Gaussian Processes
Daniel Augusto de Souza, Alexander Nikitin, ST John +6
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the…
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe, Marc Peter Deisenroth
Neural ODEs demonstrate strong performance in generative and time-series modelling. However, training them via the adjoint method is slow compared to discrete models due to the req…
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Ahmet Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu
Objects we interact with and manipulate often share similar parts, such as handles, that allow us to transfer our actions flexibly due to their shared functionality. This work addr…
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
Ahmet Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu
This paper presents a novel Learning from Demonstration (LfD) method that uses neural fields to learn new skills efficiently and accurately. It achieves this by utilizing a shared…