Reviews: AutoAssist: A Framework to Accelerate Training of Deep Neural Networks

The theoretical study of instance shrinkage in pegasos is as far as I know novel and interesting. Specially interesting is how instance shrinkage does not affect the solution the model converges to, which justifies later experiments which ignore importance sampling in deep nets. Similarly, the idea of training a small assistant model just to predict the loss of the base model on unseen examples is straightforward and potentially useful. The algorithm is clearly described, including all hyperparameters, and it does look like it should be possible to replicate the experiments. It's unclear from reading the experimental section, however, that this algorithm is actually an improvement over just regular training with no curriculum attached.