+ Clarifications: - The question of the latent variable model seems relevant and interesting. It seems that the mixup method is only as good as the model, and also the trained model might add its own biases to the classification task. It would be nice to see some discussion of this in the paper - I am surprised that mixup improves precision on the adult task. It would be good to see some exploration of this - For experiments, are all runs shown? Or just the Pareto fronts. - A number of hyperparameters (e.g. regularization) are not given - For all the latent path figures (eg Fig 3) why is the y value at x= 0 always 0? Is it normalized to this? Be clear in your description (or maybe I missed it) - I would be interested in seeing some further analysis on this model, perhaps using the interpolations themselves
- A number of hyperparameters (e.g. regularization) are not given - For all the latent path figures (eg Fig 3) why is the y value at x= 0 always 0? Is it normalized to this? Be clear in your description (or maybe I missed it) - I would be interested in seeing some further analysis on this model, perhaps using the interpolations themselves