SOURCE-LINKED INTELLIGENCE

Nonlinear Evolutions and Iterative Algorithms: Optimization and Control

CORDIS · observation · Publication date unknown

up an entirely new field in scientific computing. Furthermore, parametric IA, which are tailored to training data —making them learnable IA— are currently driving remarkable advances in computing and machine learning. However, most of these systems lack rigorous guarantees of performance or interpretability of results. The second goal of NEITALG is to formulate new learnable IA with focus on interpretability and generalization guarantees. This will greatly advance the development of safer and more reliable technologies based on machine learning. The key approach to achieve both goals involves the systematic study of the largely unexplored mathematical field connecting iterative algorithms and nonlinear evolutions (NE). NEITALG will 1. Explore the principles for which the analysis of NE provides insights into the convergence of an IA; 2. Generate new IA from nonstandard NE to overcome traditional limitations (e.g., locality, lack of interpretability); 3. Provide mean-field optimal control theories for new learnable IA; 4. Obtain guarantees for learnable IA on metric spaces; 5. Devel

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recordType

award

status

SIGNED

region

EU

value

2086498

unit

EUR