We demonstrate that plasticity loss is pervasive under domain shift in this regime, and that a number of methods developed to resolve it in other settings fail, sometimes even performing worse than applying no intervention at all. In contrast, we find that a class of "regenerative" methods are able to consistently mitigate plasticity loss in a variety of contexts, including in gridworld tasks and

We also give unique insights into its effectiveness.

Transformers have achieved great success in machine learning applications.

Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalizationaccuracy.

Neural Information Processing Systems http://nips.cc/

Groupingthechannelstogether Token-mixingMLPstake S-dimensionalvectorsasinputs.Every such vector contains values of asingle feature acrossS different spatial locations. In other words, token-mixing MLPs operate by looking at onlyone channel at once. Forstochasticdepth,followingtheoriginal paper [3], we linearly increase the probability of dropping a layer from0.0 Models are fine-tuned at resolution 224 unless mentioned otherwise. We follow the setup of [2].

Specifically,we first propose a customized homomorphic encryption-based protocol for matrix multiplication that crucially relies on a novel compact packing technique.