The technique of embedding discrete data in a continuous, moderate-dimensional space has proven useful for learning representations in many different domains. Embeddings learned from text, graphs, and human-created tags can support information retreival, recommendations, classification, and subjective human insight. In this talk I play with StarSpace, a new, open-source supervised embedding framework, and use it to learn representations of text, channels, and users.
Keith Adams is Chief Architect at Slack. Prior to Slack, he worked at Facebook, where he contributed to the search, the HipHop Virtual Machine for PHP, and Facebook AI Research. Keith was also an early engineer at VMware. He is a computing generalist, with a recurring interest in the hardware/software interface.