2 citations · 5 across the 14 of their papers we have counts for
3 papers · 2 filters
The Hidden Pitfalls of the Cosine Similarity Loss
Andrew Draganov, Sharvaree Vadgama, Erik J. Bekkers
We show that the gradient of the cosine similarity between two points goes to zero in two under-explored settings: (1) if a point has large magnitude or (2) if the points are on op…
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…
Grounding Continuous Representations in Geometry: Equivariant Neural Fields
David R Wessels, David M Knigge, Samuele Papa +4
Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a sha…