150 citations · 236 across the 7 of their papers we have counts for
7 papers
Exploring DMD-type Algorithms for Modeling Signalised Intersections
Kazi Redwan Shabab, Shakib Mustavee, Shaurya Agarwal +2
This paper explores a novel data-driven approach based on recent developments in Koopman operator theory and dynamic mode decomposition (DMD) for modeling signalized intersections.…
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…
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Tim Pearce, Mohamed Zaki, Alexandra Brintrup +1
This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should b…
Multiple Instance Learning by Discriminative Training of Markov Networks
Hossein Hajimirsadeghi, Jinling Li, Greg Mori +2
We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more…