Additionally, since the observed entries of Y are generated based on high and low enough performance, it is safe to assume that most observed entries are correct and only a few may be incorrect.
the k-th metric of the cluster is thus to build a predictor M with access to only a limited number of training samples, we make the prediction probability by linearly combining prediction from learned cluster-encoders: where f k is the learned (and frozen) encoder of the k-th cluster, {↵ k } K k=1 are adaptable parameters trained with few-shot training examples.