Topic: LocRNN

Biological neural networks use an abundance of “recurrent” connections, yet state-of-the-art deep neural network based computer vision models are predominantly feedforward. Why does biological vision evolve to employ recurrence pervasively? In this paper, we aim to address this question from a machine learning and modeling framework highlighting a critical functional advantage of employing recurrent computations: we show that a recurrent network is naturally able to adapt its computational budget flexibly during inference and generalizes in a zero-shot manner within-task across difficulties. W