European strategic research agenda in artificial intelligence
ELISE emerged from the urge to organize the world-wide revolution of machine learning in Europe. This network of excellence is motivated by the need to build, inspire and disseminate knowledge of data-driven learning in Europe. Where other continents have woken up earlier, the ELISE members are motivated by a shared concern that any participation in the world-wide competition starts from mastering the fundamentals and the time it takes to get to applications.
ELISE forms a Strategic Research Agenda (SRA) from a set of research programs, their cross-cutting themes, and links to other fields. All elements will be dynamically updated during the project. The research programs have the bottom-up interest by design, and ELISE adds facilitating actions to increase their chances of research success and maximize impact.

The SRA will be designed to be maximally useful for strategic decision-making regarding AI both on the EU level, nationally, and in the industry. It will be published widely to maximize its impact. It will be executed by the current and future research programs of the network. The progress is monitored to maximize the flow of information and results to the society and to the industry. Both the research and the support actions will be coordinated with other European networks of this same call as well as other calls. The SRA will be further developed in a series of research workshops and dedicated stakeholder workshops.

Research programs

ELISE SRA is based on the following 16 research programs:

The programme intends to develop the theoretical aspects of geometric deep learning, such as generalizations to new types of convolutions, uncovering connections to mathematical structures such as fibre bundles and group theory, and proving properties of geometric deep learning architectures. By collaborating with European industry the tools of geometric deep learning can make European companies leading in important areas such as pharmaceutics (drug design), climate modelling (CNNs on spheres), gaming (rendering on manifolds), knowledge representation (knowledge graphs), and material sciences (deep learning on meshes).

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