Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans can extrapolate simple reasoning strategies to solve difficult problems using long sequences of abstract manipulations, i.e., harder problems are solved by thinking for longer. In contrast, the sequential computing budget of feed-forward networks is limited by their depth, and networks trained on simple problems have no way of extending their reasoning capabilities without retraining. In this work, we observe that recurrent networks have the uncanny ability to closely emulate the behavior of non-recurrent deep models, often doing so with far fewer parameters, on both image classification and maze solving tasks. We also explore whether recurrent networks can make the generalization leap from simple problems to hard problems simply by increasing the number of recurrent iterations used as test time. To this end, we show that recurrent networks that are trained to solve simple mazes with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference.

本文研究了深度神经网络的层次结构特征，发现递归神经网络与前馈网络在提取特征和处理深层次信息方面表现出类似的表现和性能优越，且递归模型能够模拟前馈模型的行为并使用更少的参数来实现图像分类和迷宫问题的求解。

递归和深度的惊人相似性