17 citations · 21 across the 3 of their papers we have counts for
5 papers · 1 filter
Model Architecture Adaption for Bayesian Neural Networks
Duo Wang, Yiren Zhao, Ilia Shumailov +1
Bayesian Neural Networks (BNNs) offer a mathematically grounded framework to quantify the uncertainty of model predictions but come with a prohibitive computation cost for both tra…
Learned Low Precision Graph Neural Networks
Yiren Zhao, Duo Wang, Daniel Bates +3
Deep Graph Neural Networks (GNNs) show promising performance on a range of graph tasks, yet at present are costly to run and lack many of the optimisations applied to DNNs. We show…
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Duo Wang, Mateja Jamnik, Pietro Lio
Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems. In this work we prop…
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds
Duo Wang, Mateja Jamnik, Pietro Lio
While modern deep neural architectures generalise well when test data is sampled from the same distribution as training data, they fail badly for cases when the test data distribut…
Probabilistic Dual Network Architecture Search on Graphs
Yiren Zhao, Duo Wang, Xitong Gao +3
We present the first differentiable Network Architecture Search (NAS) for Graph Neural Networks (GNNs). GNNs show promising performance on a wide range of tasks, but require a larg…