In this paper, we introduce Distributed Lion, an innovative adaptation of Lion for distributed training environments.

However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.

Despite their different natures, these strategies often lead to comparable performance gains.

Despite its importance, the vision-language connector has been relatively less explored.