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## User: ashley-daniel-gritzman

### Title: Avoiding Implementation Pitfalls of "Matrix Capsules with EM Routing" by Hinton et al

The recent progress on capsule networks by Hinton et al. has generated
considerable excitement in the machine learning community. The idea behind a
capsule is inspired by a cortical minicolumn in the brain, whereby a vertically
organised group of around 100 neurons receive common inputs, have common
outputs, are interconnected, and may well constitute a fundamental computation
unit of the cerebral cortex. However, Hinton's paper on "Matrix Capsule with EM
Routing'" was unfortunately not accompanied by a release of source code, which
left interested researchers attempting to implement the architecture and
reproduce the benchmarks on their own. This has certainly slowed the progress
of research building on this work. While writing our own implementation, we
noticed several common mistakes in other open source implementations that we
came across. In this paper we share some of these learnings, specifically
focusing on three implementation pitfalls and how to avoid them: (1) parent
capsules with only one child; (2) normalising the amount of data assigned to
parent capsules; (3) parent capsules at different positions compete for child
capsules. While our implementation is a considerable improvement over currently
available implementations, it still falls slightly short of the performance
reported by Hinton et al. (2018). The source code for this implementation is
available on GitHub at the following URL:
https://github.com/IBM/matrix-capsules-with-em-routing.

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