Table 1: Finetuning pruned models with more training steps. Note that the only difference lies in the position of the summation. It is easy to observe that our model achieves convergence rapidly. The dataset size of LSUN Bedroom is 44.48GB, which is We conducted further investigations to explore the effectiveness of knowledge distillation in enhancing pruning techniques. Table 3 profiles the pre-trained and the pruned models on a single A5000, with a batch size of 1.

Learning rates and schedules are often chosen heuristically to address this trade-off.

The impressive capability of these models, however, often entails significant computational overhead during both training and inference.

The main observation behind our approach is that separation does not require optimization butcan besolvedinclosed-form prior totraining and plugged into a network.

Our approach relies on the principle ofoptimism under uncertaintyto efficiently explore. Our learning algorithms optimizetheiractions withrespect toamodel based ontheempirical statistics, while optimistically overestimating rewards and underestimating the resource consumption (i.e., overestimating the distance from the constraint).