1 code implementation • 23 Oct 2023 • Ross M. Clarke, Baiyu Su, José Miguel Hernández-Lobato
Research into optimisation for deep learning is characterised by a tension between the computational efficiency of first-order, gradient-based methods (such as SGD and Adam) and the theoretical efficiency of second-order, curvature-based methods (such as quasi-Newton methods and K-FAC).
1 code implementation • 23 Oct 2023 • Elre T. Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato
A truncation of this infinite series provides a new optimisation algorithm which is scalable and comparable to other first- and second-order optimisation methods in both runtime and optimisation performance.
1 code implementation • ICLR 2022 • Ross M. Clarke, Elre T. Oldewage, José Miguel Hernández-Lobato
Machine learning training methods depend plentifully and intricately on hyperparameters, motivating automated strategies for their optimisation.