Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is difficult to control.

Neural Architecture Search (NAS) has fostered the automatic discovery of state-of-the-art neural architectures. Despite the progress achieved with NAS, so far there is little attention to theoretical guarantees on NAS.

On the other hand, as the teacher has learned rich and powerful representations, the existing approaches enforce the student to fully mimic the behavior of the teacher.

Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes equations. However, despite the fast-growing field of data-driven models for physical systems, reference datasets representing real-world phenomena are lacking.