Research output per year
Research output per year
Personal profile
Overview
I am a postgraduate researcher working in the machine learning group under the supervision of Dr Mauricio Álvarez. My research is focused on Bayesian deep learning, specifically deep Gaussian processes, latent force models and combining the advantages of physically-inspired and deep probabilistic models.
Research Beacons, Institutes and Platforms
- Sustainable Futures
Fingerprint
Dive into the research topics where Thomas Baldwin-McDonald is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
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Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning
McDonald, T. M. & Álvarez , M., 24 Nov 2023.Research output: Preprint/Working paper › Preprint
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Nonparametric Gaussian Process Covariances via Multidimensional Convolutions
McDonald, T. M., Ross, M., Smith, M. T. & Alvarez, M., 20 Aug 2023, Proceedings of Machine Learning Research: International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain. Ruiz, F., Dy, J. & VanDeMeent, J. (eds.). Vol. 206. p. 8279-8293 15 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review
Open Access -
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
McDonald, T. M., Ross, M., Smith, M. T. & Álvarez, M. A., 17 Jun 2022.Research output: Preprint/Working paper › Preprint
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Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
McDonald, T. M. & Álvarez, M. A., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 13809-13819 11 p. (Advances in Neural Information Processing Systems; vol. 17).Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review
Thesis
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Bayesian Deep Learning with Physics-informed Gaussian Processes
Baldwin-McDonald, T. (Author), Rattray, M. (Supervisor) & Alvarez, M. (Supervisor), 6 Jan 2025Student thesis: Phd
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