Vortragsdetails

23. Februar 2011 - 13:00 Uhr
Von: Noelia Oses

An introduction to Hierarchical Temporal Memory and its foreseen application in ROBOFOOT and SEEGRIP


Hierarchical Temporal Memory (HTM) is a classification and pattern recognition framework modeled on the neocortex. One of the features that makes this framework different from others is its use of "time as a supervisor" to create invariant representations. For this reason, HTM models are trained with sequences of patterns (i.e. temporally correlated patterns). The underlying learning algorithms used in HTMs are not specific to particular sensory domains and thus can be applied to a broad set of problems that involve modeling complex sensory data. We will discuss possible applications of HTM in the ROBOFOOT and SEEGRIP projects.

Seminarraum 117
Robert-Hooke-Str. 5
Bremen

In der Regel sind die Vorträge Teil von Lehrveranstaltungsreihen der Universität Bremen und nicht frei zugänglich. Bei Interesse wird um Rücksprache mit dem Sekretariat unter sek-ric(at)dfki.de gebeten.

© DFKI GmbH
zuletzt geändert am 12.07.2019
nach oben