Mr Tim Pearce
Tim is a PhD student researching,
- Uncertainty in neural networks (NNs) / deep learning
- Bayesian NNs, ensembled NNs, Gaussian Processes.
- Uncertainty in Reinforcement Learning.
- Applications of the above to manufacturing data.
He spent one year as an exchange student at the Alan Turing Institute.
Pearce, T., Zaki, M., Brintrup, A., & Neely, A. (2019). Uncertainty in Neural Networks: Bayesian Ensembling. Under Review.
Pearce, T., Zaki, M., Brintrup, A., & Neely, A. (2018). Bayesian Neural Network Ensembles. Bayesian Deep Learning Workshop, NeurIPS (NIPS).
Pearce, T., Zaki, M., Brintrup, A., & Neely, A. (2018). High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach. In Proceedings of the 35th International Conference on Machine Learning, ICML. Stockholm.
Pearce, T., Anastassacos, N., Zaki, M., & Neely, A. (2018). Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning. In Exploration in Reinforcement Learning Workshop, ICML. Stockholm.
Palau, A. S., Bakliwal, K., Dhada, M. H., Pearce, T., & Parlikad, A. K. (2018). Recurrent Neural Networks for real-time distributed collaborative prognostics. In IEEE International Conference on Prognostics and Health Management (ICPHM).