Colloquium: Multimodal Learning of Actions with Deep Neural Network Self-Organization

10 March 2017

Understanding other people's actions plays a crucial role in our everyday lives. Human beings are able to reliably discriminate a series of socially relevant cues from body motion, with this ability being supported by highly skilled visual perception and other modalities.

The main goal of this thesis is the modeling of artificial learning architectures for action perception with focus on the development of multimodal action representations. As a modeling foundation to address our research question, we focus on hierarchies of self-organizing neural networks motivated by experience-driven cortical organization.

Friday, 10 Marchl 2017, 14:00, Informatik, Room F-334

German Ignacio Parisi, PhD candidate at group WTM, University of Hamburg