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cs.LG2023
Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens
Ross M. Clarke, 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 th…
cs.LG2023
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks
Elre T. Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato
Despite their popularity in the field of continuous optimisation, second-order quasi-Newton methods are challenging to apply in machine learning, as the Hessian matrix is intractab…
cs.LG2021
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation
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. Many existing algorithms restart tr…