Reviews: Learning long-range spatial dependencies with horizontal gated recurrent units

This task – the Pathfinder Challenge – has been used in the neuroscience literature and requires deciding whether two dots in an image are connected by a path made of short line segments. They show that a single layer of hGRU can solve this task almost perfectly, while CNNs need to be quite deep to achieve comparable performance and require orders of magnitude more parameters. Strengths: The paper is well motivated and conceptually very clear. The Pathfinder challenge uses simple images to generate a non-trivial and interesting task. The paper shows a limitation of CNNs and proposes an effective solution using a gated recurrent model. The result that a one-layer recurrent model can solve the task is quite remarkable. Ablation studies and comparisons with other models show that the proposed hGRU model maximizes the ratio of performance to the number of parameters. Weaknesses: - The hGRU architecture seems pretty ad-hoc and not very well motivated.