2 citations · 3 across the 2 of their papers we have counts for
8 papers
Learning Small Molecule Energies and Interatomic Forces with an Equivariant Transformer on the ANI-1x Dataset
Bryce Hedelius, Fabian B. Fuchs, Dennis Della Corte
Accurate predictions of interatomic energies and forces are essential for high quality molecular dynamic simulations (MD). Machine learning algorithms can be used to overcome limit…
Universal Approximation of Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke +2
Modelling functions of sets, or equivalently, permutation-invariant functions, is a long-standing challenge in machine learning. Deep Sets is a popular method which is known to be…
Iterative SE(3)-Transformers
Fabian B. Fuchs, Edward Wagstaff, Justas Dauparas +1
When manipulating three-dimensional data, it is possible to ensure that rotational and translational symmetries are respected by applying so-called SE(3)-equivariant models. Protei…
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer +1
We introduce the SE(3)-Transformer, a variant of the self-attention module for 3D point clouds and graphs, which is equivariant under continuous 3D roto-translations. Equivariance…
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning
Fabian B. Fuchs, Adam R. Kosiorek, Li Sun +2
The majority of contemporary object-tracking approaches do not model interactions between objects. This contrasts with the fact that objects' paths are not independent: a cyclist m…
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke +2
Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance. In particular, it has been conjectur…