Objective Machine learning's goal is to devise algorithms that improve with experience. Currently, experience is largely defined to be the amount of available data. Unfortunately, acquiring data can be time consuming (e.g. annotating documents), monetarily expensive (e.g. genetic testing), physically invasive (e.g. collecting a tissue sample) or unavailable in sufficient quantities (e.g. data about rarediseases). For some tasks, this makes it challenging to obtain the quantities of data necessary to build a sufficiently accurate predictive model. Machine learning algorithms are applicable to manydomains, but cannot generalize across different domains because of the underlying assumption that the training (used to learn the model) and test (used to evaluate the model) data come from the samedistribution. However, in the real world this is often not the case. People are much more adept at handling this than machines and are even able to reapply knowledge learned in one domain to anentirely different one. Yet standard machine learning approaches are unable to do this. Computationally, the missing link is the ability to discover structural regularities that apply to many different domains, irrespective of their superficial descriptions. This is arguably the biggest gap between current machine learning systems and humans. To address this problem, algorithms must be able to perform deep transfer, which involves generalizing across entirely different domains (i.e. between domains with different objects, classes, properties and relations). Few learning algorithms are able to do this. In this project, we will attempt to develop a well-founded, fully automatic approach to deep transfer that discerns complex structural regularities and determines which of theseproperties are likely to apply to a given target task. Deep transfer offers a fundamentally different and novel paradigm for acquiring experience: exploiting data from other, possibly very different, tasks. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Topic(s) FP7-PEOPLE-2011-CIG - Marie-Curie Action: "Career Integration Grants" Call for proposal FP7-PEOPLE-2011-CIG See other projects for this call Funding Scheme MC-CIG - Support for training and career development of researcher (CIG) Coordinator KATHOLIEKE UNIVERSITEIT LEUVEN EU contribution € 100 000,00 Address OUDE MARKT 13 3000 Leuven Belgium See on map Region Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven Activity type Higher or Secondary Education Establishments Administrative Contact Stijn Delauré (Dr.) Links Contact the organisation Opens in new window Website Opens in new window Total cost No data