The significant heterogeneity poses higher requirements foralgorithm robustness.

Alldatausedispublic.] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they werechosen)? A.1 TrainingDetails In our experiments, the classifierfθ is a 8-layer MLP with 128 hidden dimensions per layer.

Although deep learning programs havedemonstrated strong performance on novel applications, they sacrifice many of the functionalities of traditional software programs.

Figure 11: RecBeerenvironments: Each year serves as a different environment, whose affect is expressed through differing correlations between beer types and user choices.

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

Whether when planning our paths home from the office or from a hotel to an airport in an unfamiliar city, we typically focus on a small subset of relevant variables,e.g. the changeinposition orthepresence oftraffic.