We recently talked about Capsules networks and equivariances. NB: If you’re not familiar with Capsules networks, read this first. Our primary objective with Capsules networks is to exploit their enhanced generalization abilities. However, what we’ve found instead raises new questions about how generalization can be measured and whether Capsules networks are… Read More »Predictive Capsules Networks – Research update
We are exploring the nature of equivariance, a concept that is now closely associated with the capsules network architecture (see key papers Sabour et al, and Hinton et al). Machine learning representations that capture equivariance must learn the way that patterns in the input vary together, in addition to statistical clusters in… Read More »Understanding Equivariance
We’ve just uploaded a spin-off research paper to arXiv titled “Sparse Unsupervised Capsules Generalize Better”. So what’s it all about? Capsules Networks You may have heard of Capsules Networks already – if not, have a read of one of these blog articles (here, here, here, or here (EM routing)), watch this video,… Read More »Sparse Unsupervised Capsules Generalize Better