Research output per year
Research output per year
Personal profile
Research Interests
I am interested in probabilistic inference and learning from both a machine learning and neuroscience perspective. In particular I will be exploring how sampling-based probabilistic inference and learning might be implemented within biological neural networks.
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Dive into the research topics where Matthew Graham is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Continuously tempered Hamiltonian Monte Carlo
Graham, M. & Storkey, A., 15 Aug 2017, The Conference on Uncertainty in Artificial Intelligence (UAI 2017). Sydney, Australia, 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile -
Asymptotically exact inference in differentiable generative models
Graham, M. & Storkey, A., 22 Apr 2017, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017. PMLR, p. 499-508 10 p. (Proceedings of Machine Learning Research; vol. 54).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile -
Continuously tempered Hamiltonian Monte Carlo
Graham, M. & Storkey, A., 9 Dec 2016.Research output: Contribution to conference › Abstract › peer-review
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Pseudo-Marginal Slice Sampling
Murray, I. & Graham, M., May 2016, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics 2016. Cadiz, Spain: PMLR, Vol. 51. p. 911-919 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile -
Evidence of increased UV Fe II emission in quasars in candidate overdense regions
Harris, K. A., Williger, G. M., Haberzettl, L., Mitchell, S., Farrah, D., Graham, M. J., Davé, R., Younger, M. P. & Söchting, I. K., 6 Sep 2013, In: Monthly Notices of the Royal Astronomical Society. 435, 4, p. 3125-3132Research output: Contribution to journal › Article › peer-review
Open Access