We propose a metric that can measure the influence of a node in a layer of a neural network to a node in subsequent layers. The metric, called influential scores, enables us to interpret the effect of ...

The implications are that for problems with hierarchical locality, such as image classification, deep networks are exponentially more powerful than shallow networks.

Above is a visualization of the current Lightning Network topography made up of ~16,000 Lightning Nodes with ~140,000 payment channels opened between them. I don’t know if I’m simply being duped by ...

Given the similarity between image and graph domains, we analyze the adaptability of prototype-based neural networks for graph and node classification. In particular, we investigate the use of two ...

Two separate groups of researchers at Google and Stanford have merged best-of-breed neural network models and created systems that can accurately explain what’s happening in images.