A Unified Theory of Diversity in Ensemble Learning
Wood, Danny, et al. "A Unified Theory of Diversity in Ensemble Learning." arXiv preprint arXiv:2301.03962 (2023).
Publications
All papers can be downloaded by clicking on the pdf symbol beneath them.
Bias-Variance Decompositions for Margin Losses
Wood, Danny, Tingting Mu, and Gavin Brown. "Bias-Variance Decompositions for Margin Losses." International Conference on Artificial Intelligence and Statistics. PMLR, 2022.
Two-stage Classification for Detecting Murmurs from Phonocardiograms Using Deep and Expert Features
Summerton, Sara, et al. "Two-stage Classification for Detecting Murmurs from Phonocardiograms Using Deep and Expert Features." Computing in Cardiology 2022: 49th Computing in Cardiology Conference. 2022.
Submitted as an entry in the 2022 Physionet Challenge as member of the team Murmur Mia! (winner of best team name). Hidden Markov Model segmentation code used in the paper can be found here.
Effects of Network Weight Structure in Echo State Networks
Wood, Danny (2020). "Effects of Network Weight Structure in Echo State Networks." PhD Thesis.
My thesis explored memory and stability in Echo State Networks. Drawing on tools from control theory, I gave exact expressions for calculating the memory capacity of these networks. I also looked at the “different timescales” phenomenon in recurrent neural networks, characterising the phenomenon both in terms of the memory capacity of deep network layers and in terms of how the sensitivity of different layers to perturbations in input varies over time.