We are very excited to join forces with OctoML.ai! Contact Grigori Fursin for more details!

A Computational Theory for Life-Long Learning of Semantics

lib:a608cf7eb50aa274 (v1.0.0)

Authors: Peter Sutor Jr.,Douglas Summers-Stay,Yiannis Aloimonos
ArXiv: 1806.10755
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1806.10755v2


Semantic vectors are learned from data to express semantic relationships between elements of information, for the purpose of solving and informing downstream tasks. Other models exist that learn to map and classify supervised data. However, the two worlds of learning rarely interact to inform one another dynamically, whether across types of data or levels of semantics, in order to form a unified model. We explore the research problem of learning these vectors and propose a framework for learning the semantics of knowledge incrementally and online, across multiple mediums of data, via binary vectors. We discuss the aspects of this framework to spur future research on this approach and problem.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!