fb2697869f56484404c8ceee2985b01d-AuthorFeedback.pdf

"blur the distributions": As Wasserstein barycenter adjusts the support, blurring is more likely for Euclidean V anilla averaging, in contrast, fails to fine-tune despite trying numerous settings of optimization hyperparameters. Also, Fig 1, shows similar gains for data-free post-processing in case of structured pruning (as in Sec 5.2). V anilla average fails to retrain. Results shown are mean std. "there could possibly be more competent baselines": The'constraint' of performing this without sharing of sensitive training data arises in many applications, "improvement over vanilla averaging is very marginal": We respectfully disagree.