However, if intrinsic parameters change, neural computations are altered drastically.

Hinton et al. first propose the concept of distillation, where a lightweight student model

During optimization, contrastive learning keeps the different modalities separated by a certain distance, which is influenced by the temperature parameter in the loss function. Our experiments further demonstrate that varying the modality gap distance has a significant impact in improving the model's downstream zero-shot classification performance and fairness.

In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics.

In this natural setting, it is not common to see the same data sample multiple times, or even twice at times.

A.1 Algorithms We provide algorithms to describe the algorithmic contributions in the main paper.

Moreover,weshow that their inductive biases are poorly matched to lifelong learning formulations ofclassification and translation.