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What and Who
Title:Learning with Memory Embeddings
Speaker:Volker Tresp
coming from:Siemens Research and LMU Munich
Speakers Bio:
Event Type:Talk
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Expert Audience
Date, Time and Location
Date:Thursday, 14 April 2016
Duration:60 Minutes
Building:E1 4
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.
Name(s):Petra Schaaf
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Attachments, File(s):
Petra Schaaf/AG5/MPII/DE, 04/13/2016 09:55 AM
Last modified:
Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Petra Schaaf, 04/13/2016 10:00 AM
  • Petra Schaaf, 04/13/2016 09:58 AM -- Created document.