Design and development of generic models capable of adapting to learn to perform a specific task. To this end, these models have a set of parameters that determine their behaviour and are adjusted to the appropriate value by means of a training process and on the basis of a set of data representative of the task to be learned. Basically, in this process, the computer discovers how to solve the task according to the empirical data provided to it.
SERVICE DESCRIPTION
Design and development of generic models capable of adapting to learn to perform a specific task. To this end, these models have a set of parameters that determine their behaviour and are adjusted to the appropriate value by means of a training process and on the basis of a set of data representative of the task to be learned. Basically, in this process, the computer discovers how to solve the task according to the empirical data provided to it. We can distinguish between supervised, unsupervised, semi-supervised and reinforcement learning techniques. The tasks that these algorithms can learn can also be stated, the most typical of which are classification, prediction, clustering, sorting and anomaly detection. Some applications include: Anomaly detection techniques
Real-time and continuous learning
Privacy-preserving distributed learning of data at individual sites. The aim is to enable learning with data from different partners without compromising privacy between them.
Learning techniques based on component analysis
Evolutionary computing and bio-inspired or natural computation metaheuristics
Development of 'lightweight' models and algorithms for low-capacity devices and in connected environments (IoT)
Feature engineering learning using perturbation theory in Bioinformatics/Chemoinformatics
Development of algorithms that take fairness issues into account
Explainability in machine learning
Machine learning with quantum computing
Human-in-the-loop machine learning
Machine learning for brain-computer interfaces
Behavioural learning and optimisation in mobile and autonomous robotics
Self-supervised and weakly supervised learning.
Intelligent signal processing in mobile devices.
Deep learning and new neural network architectures.
Analysis of complex biological systems with machine learning and data integration techniques.
Deterministic symbolic regression
Computational modelling
Deep learning for perception processing tasks, with focus on computational and environmental efficiency.
Multi-task learning techniques for natural language
Applications: video surveillance, image optimisation, music generation, signal analysis (especially medical signals), agriculture and food quality.
SPECIAL ACCESS CONDITIONS
No
