Compared withconvolutional neural networks (CNNs), the training ofAdderNets ismuch more sophisticated including several techniques for adjusting gradient and batch normalization.

In Restless-UCB, we present a novel method to construct offline instances,whichonlyrequiresO(N)time-complexity(N isthenumberofarms) and is exponentially better than the complexity of existing learning policy.

Meta-learning improvesgeneralization ofmachine learning models when faced with previously unseen tasks by leveraging experiences from different, yet related prior tasks.

However, current approaches can only model distributions for which training samples are directly accessible, which is not the case in many real-world tasks.