270 citations · 315 across the 9 of their papers we have counts for
6 papers · 1 filter
Equilibrium Aggregation: Encoding Sets via Optimization
Sergey Bartunov, Fabian B. Fuchs, Timothy Lillicrap
Processing sets or other unordered, potentially variable-sized inputs in neural networks is usually handled by aggregating a number of input tensors into a single representation. W…
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…
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs +2
This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph…
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…
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…