title: How Important is a Neuron
abstract: The problem of attributing a deep network’s prediction to its input/base features is
  well-studied (cf. Simonyan et al. (2013)). We introduce the notion of conductance
  to extend the notion of attribution to understanding the importance of hidden units.
  Informally, the conductance of a hidden unit of a deep network is the flow of attribution
  via this hidden unit. We can use conductance to understand the importance of
  a hidden unit to the prediction for a specific input, or over a set of inputs. We justify
  conductance in multiple ways via a qualitative comparison with other methods,
  via some axiomatic results, and via an empirical evaluation based on a feature
  selection task. The empirical evaluations are done using the Inception network
  over ImageNet data, and a convolutinal network over text data. In both cases, we
  demonstrate the effectiveness of conductance in identifying interesting insights
  about the internal workings of these networks.
authors: Kedar Dhamdhere Mukund Sundararajan Qiqi Yan
hIndex: Mukund Sundararajan 24