Project description
Theoretical foundations for a new machine learning branch
Funded by the Marie Skłodowska-Curie Actions programme, the LEMUR project will develop the theoretical foundations and a first set of algorithms for a novel branch of machine learning (ML) called learning with multiple representations (LMR). These LMR algorithms will allow flexible representations (simple and fair) with diverse target functions (environmental and social impact) to ensure they are in line with the Green Charter and trustworthy AI criteria by design. The project will focus on learning with weak supervision as it addresses one of the major flaws of modern ML approaches. LEMUR will provide 10 experts with highly interdisciplinary and intersectoral training to implement the third and subsequent waves of AI in Europe.
Objective
Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g. as a classification problem), including a specification of the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to create a novel branch of machine learning we call Learning with Multiple Representations. We aim to develop the theoretical foundations and a first set of algorithms for this new paradigma. Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. We regard LEMUR as very timely, as LMR algorithms will allow to flexible representations (e.g. suitable for explainability, fairness) with diverse target functions (e.g. incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. We will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e. their data hunger, by means of weaker sources of labelling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competences relevant to successful careers in central AI areas.
Fields of science
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral NetworksCoordinator
33098 Paderborn
Germany
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Participants (9)
33615 Bielefeld
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80539 MUNCHEN
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60-965 POZNAN
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3000 Leuven
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20126 MILANO
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1678 Nicosia
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53100 Siena
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1081 HV Amsterdam
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901 87 Umea
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Partners (10)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
5656 AG Eindhoven
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75018 Paris
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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50127 Firenze
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90429 Nurnberg
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69115 Heidelberg
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3011 Lemesos
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931 85 Skelleftea
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92190 MEUDON
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
1043NX Amsterdam
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
D15 HN66 DUBLIN
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