13 citations · 16 across the 2 of their papers we have counts for
6 papers
Structured Weight Priors for Convolutional Neural Networks
Tim Pearce, Andrew Y. K. Foong, Alexandra Brintrup
Selection of an architectural prior well suited to a task (e.g. convolutions for image data) is crucial to the success of deep neural networks (NNs). Conversely, the weight priors…
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Russell Tsuchida, Tim Pearce, Chris van der Heide +2
Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance fu…
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
Tim Pearce, Russell Tsuchida, Mohamed Zaki +2
A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e.g. summing a periodic and linear…
Bayesian Neural Network Ensembles
Tim Pearce, Mohamed Zaki, Andy Neely
Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differi…
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Tim Pearce, Felix Leibfried, Alexandra Brintrup +2
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying…
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning
Tim Pearce, Nicolas Anastassacos, Mohamed Zaki +1
The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. However, the current justification is intuitive rather than analytical…