9 citations · 12 across the 10 of their papers we have counts for
10 papers
E(n) Equivariant Message Passing Cellular Networks
Veljko Kovač, Erik J. Bekkers, Pietro Liò +1
This paper introduces E(n) Equivariant Message Passing Cellular Networks (EMPCNs), an extension of E(n) Equivariant Graph Neural Networks to CW-complexes. Our approach addresses tw…
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…
Can strong structural encoding reduce the importance of Message Passing?
Floor Eijkelboom, Erik Bekkers, Michael Bronstein +1
The most prevalent class of neural networks operating on graphs are message passing neural networks (MPNNs), in which the representation of a node is updated iteratively by aggrega…
On genuine invariance learning without weight-tying
Artem Moskalev, Anna Sepliarskaia, Erik J. Bekkers +1
In this paper, we investigate properties and limitations of invariance learned by neural networks from the data compared to the genuine invariance achieved through invariant weight…
Learned Gridification for Efficient Point Cloud Processing
Putri A. van der Linden, David W. Romero, Erik J. Bekkers
Neural operations that rely on neighborhood information are much more expensive when deployed on point clouds than on grid data due to the irregular distances between points in a p…