  Without relevant human priors, neural networks may learn uninterpretable
features. We propose Dynamics of Attention for Focus Transition (DAFT) as a
human prior for machine reasoning. DAFT is a novel method that regularizes
attention-based reasoning by modelling it as a continuous dynamical system
using neural ordinary differential equations. As a proof of concept, we augment
a state-of-the-art visual reasoning model with DAFT. Our experiments reveal
that applying DAFT yields similar performance to the original model while using
fewer reasoning steps, showing that it implicitly learns to skip unnecessary
steps. We also propose a new metric, Total Length of Transition (TLT), which
represents the effective reasoning step size by quantifying how much a given
model's focus drifts while reasoning about a question. We show that adding DAFT
results in lower TLT, demonstrating that our method indeed obeys the human
prior towards shorter reasoning paths in addition to producing more
interpretable attention maps. Our code is available at
https://github.com/kakao/DAFT.
