MIN Faculty
Department of Informatics
Knowledge Technology


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Xavier Hinaut

Postdoctoral Marie Curie Fellow at Knowledge Technology Group

Contact Info

Address University of Hamburg
Department of Computer Science
Knowledge Technology, WTM, Haus F
Vogt Koelln Str. 30
22527 Hamburg, Germany
Office: F-214
Phone: +49 40 42883 2522
Fax: +49 40 42883 2515
Email: xavier.hinaut the_at_symbol informatik.uni-hamburg.de
Webpage: www.xavierhinaut.com

Short Curriculum Vitae

Jan 2013 PhD in Computer Science, Cortical Networks for Cognitive Interaction team, INSERM - Stem Cell and Brain Research Institute, University of Lyon I, France
Since 2015 Marie Curie Fellow, Knowledge Technology Research Group, Department of Computer Science, University of Hamburg, Germany

NEW! HiWi student job offer (available as soon as possible):
More info here.

NEW! IJCAI 2015 video:
"Humanoidly Speaking": How the Nao humanoid robot can learn the name of objects and interact with them through common speech.
IJCAI 2015 video competition link.
Software: Take a look at "Syntactic Reservoir Model" and "DOCKS" on the sofware webpage to download and try the source code to make the same experiment.

Several research topics for bachelor/master students available: Look for "How children learn languages? Recurrent Neural Networks to enable robots to learn (any) language!" at the Research Topics for Students webpage or look at this pdf for more details.

Marie Curie IEF project
"EchoRob": Echo State Networks for Developing Language Robots

Project webpage

In this project, we propose to build a biologically plausible model of sentence comprehension based on recurrent neural networks called reservoirs. Human sentence comprehension is mainly handled by prefrontal cortex areas, which have highly recurrent connectivity. In both biological and artificial neural networks this recurrence is supposed to enable the management of different aspects of time such as working memory and contextual information processing.
Here, we propose to develop the Reservoir Computing (RC) paradigm — in particular Echo State Networks (ESN) with incremental learning — to model language comprehension at the sentence level given sequential inputs of words or phonemes. Based on our initial research, a model processing syntactic sentence structures was able to demonstrate generalisation and online prediction capabilities while processing sequential input. For less frequent inputs, the model provided potential explanation for human electrophysiological data. Building on this research, a new model is proposed with the following objectives:
(1) processing of all semantic information enabling contextual processing and richer representation of meaning,
(2) implementing incremental learning with noisy supervision enabling realistic developmental language acquisition from simple to complex sentences,
(3) demonstrating that the model can learn from naïve user's utterances in several languages, and
(4) demonstrating the ability of this model when embodied in a robot to acquire extended language capabilities through human-robot interaction accounting for developmental schemes.

See more on my personal webpage: www.xavierhinaut.com