University of Cambridge > Talks.cam > Machine Learning @ CUED > Learning with Memory Embeddings

Learning with Memory Embeddings

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Louise Segar.

Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Latent variable models are well suited to deal with the high dimensionality and sparsity of typical knowledge graphs and have successfully been employed in knowledge graph completion and fact extraction from the Web. We have extended the approach to also consider temporal evolutions, temporal patterns and subsymbolic representations, which permits us to model medical decision processes. In addition, we consider embedding approaches to be a possible basis for modeling cognitive memory functions, in particular semantic and concept memory, episodic memory, sensory memory, short-term memory, and working memory.

This talk is part of the Machine Learning @ CUED series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity