27 citations · 46 across the 6 of their papers we have counts for
7 papers
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…
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
Kara Lamb, Garima Malhotra, Athanasios Vlontzos +7
High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes. These interactions o…
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
Kara Lamb, Garima Malhotra, Athanasios Vlontzos +7
A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weath…
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…
Batch Selection for Parallelisation of Bayesian Quadrature
Ed Wagstaff, Saad Hamid, Michael Osborne
Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive d…