Here, we ask whether we can automatically obtain natural language explanations for black box text modules.
Overall, these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
All hand-object interaction is controlled by forces that the two bodies exert on each other, but little work has been done in modeling these underlying forces when doing pose and contact estimation from RGB/RGB-D data.
Earlier work has demonstrated that dependencies in natural language tend to decay with distance between words according to a power law.
In this work, we explore the delayed-RNN, which is a single-layer RNN that has a delay between the input and output.