9 citations · 10 across the 3 of their papers we have counts for
3 papers
Space-Time Continuous PDE Forecasting using Equivariant Neural Fields
David M. Knigge, David R. Wessels, Riccardo Valperga +4
Recently, Conditional Neural Fields (NeFs) have emerged as a powerful modelling paradigm for PDEs, by learning solutions as flows in the latent space of the Conditional NeF. Althou…
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Samuele Papa, David M. Knigge, Riccardo Valperga +4
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex…
Towards a General Purpose CNN for Long Range Dependencies in D
David W. Romero, David M. Knigge, Albert Gu +4
The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learn…