Prediction APIs offered for afee area fast-growing industry and an important partofmachine learning asaservice.

To address these issues, we introduce a novel Bayesian meta-learning approach called meta-variational dropout (MetaVD).

Although recently proposed parameter-efficient transfer learning (PETL) techniques allowupdating asmallsubsetofparameters (e.g. This is because the gradient computation for the trainable parameters still requires backpropagation through thelargepre-trained backbone model.

Work todate has highlighted the challenges inmitigating bias within these datasets.