PinnedPublished inTDS ArchiveTrain ImageNet without Hyperparameters with Automatic Gradient DescentTowards architecture-aware optimisationApr 19, 2023Apr 19, 2023
Published inTDS ArchiveWhat can flatness teach us: understanding generalisation in Deep Neural NetworksThis is the third post in a series summarising work that seeks to provide a theory of generalisation in Deep Neural Networks (DNNs)…Mar 29, 2021Mar 29, 2021
Published inTDS ArchiveDeep Neural Networks are biased, at initialisation, towards simple functionsAnd why is this a very important step in understanding why they work?Jan 1, 2021Jan 1, 2021
Published inTDS ArchiveNeural networks are fundamentally BayesianStochastic Gradient Descent approximates Bayesian samplingDec 19, 2020A response icon2Dec 19, 2020A response icon2
