Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems

ABSTRACTDespite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the exact same training data could produce very different prediction results. People have relied on the state-of-the-art ensemble method to estimate prediction uncertainty. However, ensembles are expensive to train and serve for web-scale traffic systems. In this paper, we seek to advance the understanding of prediction variation estimated by the ensemble method. Through empirical experiments on two widely used benchmark datasets Movielens and Criteo in recommender systems, we observe that prediction variations come from various randomness sources, including training data shuffling, and random initialization. When we add more randomness sources to ensemble members, we see higher prediction variations among these ensemble members, and more accurate mean prediction. Moreover, we propose to infer prediction variation from neuron activation strength and demonstrate its strong prediction power. Our approach provides a simple way for prediction variation estimation, and opens up new opportunities for future work in many interesting areas (e.g., model-based reinforcement learning) without relying on serving expensive ensemble models.