16 citations · 37 across the 6 of their papers we have counts for
6 papers
Mathematical modelling of adjuvant-enhanced active ingredient leaf uptake of pesticides
J. Delos Reyes, T. Shardlow, M. B. Delgado-Charro +2
The global importance of effective and affordable pesticides to optimise crop yield and to support health of our growing population cannot be understated. But to develop new produc…
GRAND: Graph Neural Diffusion
Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova +3
We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an…
Statistically Robust Neural Network Classification
Benjie Wang, Stefan Webb, Tom Rainforth
Despite their numerous successes, there are many scenarios where adversarial risk metrics do not provide an appropriate measure of robustness. For example, test-time perturbations…
A Statistical Approach to Assessing Neural Network Robustness
Stefan Webb, Tom Rainforth, Yee Whye Teh +1
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate th…
Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb, Adam Golinski, Robert Zinkov +4
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversi…
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart +4
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to e…