33 citations · 118 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
Reconstruction Bottlenecks in Object-Centric Generative Models
Martin Engelcke, Oiwi Parker Jones, Ingmar Posner
A range of methods with suitable inductive biases exist to learn interpretable object-centric representations of images without supervision. However, these are largely restricted t…
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones +1
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects…
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…
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Adam R. Kosiorek, Hyunjik Kim, Ingmar Posner +1
We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the…