In the few-shot setting instead, we assume that some data for the target language D e is available, so we treat the posterior as a prior over the weights and update the weights accordingly.
we maximize the log-likelihood of the target language data and the regularizer derived through the Laplace Approximation scaled by a factor λ: As a baseline for the UNIV prior, we perform Maximum A Posteriori inference with an uninformative prior N (0, 1).