We argue that a natural use case of offline RL is in settings where we can pool large amounts of data collected in various scenarios for solving different tasks, and utilize all of this data to learn behaviors for all the tasks more effectively rather than training each one in isolation.
Specifically, we replace the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module.
Ranked #132 on Image Classification on ImageNet
Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive.
Ranked #1 on Grammatical Error Correction on GMEG-wiki
We present a method of generating a collection of neural cellular automata (NCA) to design video game levels.
Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available.