Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF or MPI-SWS or Local Campus Event Calendar

<< Previous Entry Next Entry >> New Event Entry Edit this Entry Login to DB (to update, delete)
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
We use this to send out email in the morning.
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:
Tags, Category, Keywords and additional notes
Attachments, File(s):
Petra Schaaf/AG5/MPII/DE, 04/13/2016 09:55 AM
Last modified:
halma/MPII/DE, 11/07/2018 04:52 PM
  • Petra Schaaf, 04/13/2016 10:00 AM
  • Petra Schaaf, 04/13/2016 09:58 AM -- Created document.