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Mr Tim Pearce

Contact information

Research interests

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.

Publications

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).

About us

The Cambridge Big Data Strategic Research Initiative brings together researchers from across the University to address challenges presented by our access to unprecedented volumes of data. Our research spans all six Schools of the University, from the underlying fundamentals in mathematics and computer science, to applications ranging from astronomy and bioinformatics, to medicine, social science and the humanities

In parallel, our research addresses important issues around law, ethics and economics, in order to apply Big Data to solve challenging problems for society.

Cambridge Big Data supports collaboration and knowledge transfer in this growing field. 

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