150 citations · 309 across the 13 of their papers we have counts for
4 papers · 1 filter
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection
Bang Xiang Yong, Tim Pearce, Alexandra Brintrup
After an autoencoder (AE) has learnt to reconstruct one dataset, it might be expected that the likelihood on an out-of-distribution (OOD) input would be low. This has been studied…
Understanding Softmax Confidence and Uncertainty
Tim Pearce, Alexandra Brintrup, Jun Zhu
It is often remarked that neural networks fail to increase their uncertainty when predicting on data far from the training distribution. Yet naively using softmax confidence as a p…
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…
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…