Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset ...

This calls for the need to have robust and data efficient deep learning models. In this work, we propose a deep learning approach called Multi-Expert Adversarial Regularization learning (MEAR) with ...

We investigate a class of reinforcement learning dynamics where players adjust their strategies based on their actions' cumulative payoffs over time—specifically, by playing mixed strategies that ...