Appendix A Posterior Reparameterization

In this section we motivate the design choices and inductive biases that we encode into our neural encoder network e, which is the network that is used to model the relative accuracies of the weak supervision sources λ. Recall that we model the probability of a particular sample x X having the class label y Y = {1,..., C} as P Our own parameterization therefore is a more expressive variant of these latent-variable PGM models, where we are able to assign LF accuracies on a sample-by-sample basis. Furthermore, our neural encoder network outputs them as a function of the LF outputs and features, and is expected to learn the easy to misspecify dependencies and label-independent statistics implicitly. The top 2 performance scores are highlighted as First, Second. Triplet-median [11] is not listed as it only converged for IMDB with 12 LFs (F1 = 73.0