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Group by: Type | Date
Jump to: Proceedings paper | Thesis
Number of items: 4.
Proceedings paper
Botev, A;
Ritter, J;
Barber, D;
(2017)
Practical Gauss-Newton Optimisation for Deep Learning.
In: Precup, D and Teh, YW, (eds.)
Proceedings of the 34th International Conference on Machine Learning.
(pp. pp. 557-565).
Proceedings of Machine Learning Research: Sydney, Australia.
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Ritter, H;
Botev, A;
Barber, D;
(2018)
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS 2018).
Neural Information Processing Systems (NIPS): Montréal, Canada.
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Ritter, H;
Botev, A;
Barber, D;
(2018)
A Scalable Laplace Approximation for Neural Networks.
In:
6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings.
International Conference on Representation Learning: Vancouver, Canada.
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Thesis
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Ritter, Julian Hippolyt;
(2023)
Scalable approximate inference methods for Bayesian deep learning.
Doctoral thesis (Ph.D), UCL (University College London).
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