DNN pruning is an approach for deep model compression, which aimsateliminating someparameters withtolerable performance degradation.

Here, we show how to benefit from the flexibility of RNNs while representing individual differences inalow-dimensional andinterpretable space. Toachievethis, wepropose anovelend-to-end learning frameworkinwhich an encoder istrained to map the behavior of subjects into alow-dimensional latent space.

To achieve both efficient and high-quality synthesis, various distillation-based accelerated sampling methods have been developed recently.

When different object types are present in the environment, we also include static object features in the object statessit. This can be viewed as a concatenation of a dynamicandastaticgraph[44]. Fortheextrinsic phase, we take the learned model with the listed architectural settings to solve downstream tasks zero-shot. In both environments 2000 transitions are generated within one trainingiterationofCEE-US. The actuated agent, i.e. robot, state is given bysagent.