Reconciling Classical and Modern (Deep) Machine Learning for Real-World Applications
APHELEIA aims to create robust, interpretable, and efficient machine learning models that require less data by integrating classical methods with modern deep learning, fostering interdisciplinary collaboration.
Projectdetails
Introduction
Despite the undeniable success of machine learning in addressing a wide variety of technological and scientific challenges, the current trend of training predictive models with an ever-growing number of parameters from an ever-growing amount of data is not sustainable.
Challenges of Current Models
These huge models, often engineered by large corporations benefiting from substantial computational resources, typically require learning a billion or more parameters. They have proven to be very effective in solving prediction tasks in various fields such as:
- Computer vision
- Natural language processing
- Computational biology
However, they mostly remain black boxes that are hard to interpret, computationally demanding, and not robust to small data perturbations.
Objectives of APHELEIA
With a strong emphasis on visual modeling, the grand challenge of APHELEIA is to develop a new generation of machine learning models that are:
- More robust
- Interpretable
- Efficient
- Not reliant on massive amounts of data to produce accurate predictions
To achieve this objective, we will foster new interactions between:
- Classical signal processing
- Statistics
- Optimization
- Modern deep learning
Methodology
Our goal is to reduce the need for massive data by enabling scientists and engineers to design trainable machine learning models that:
- Directly encode a priori knowledge of the task semantics and data formation process
- Automatically prefer simple and stable solutions over complex ones
These models will be built on solid theoretical foundations with convergence and robustness guarantees, which are important for making trustworthy predictions in real-life scenarios.
Implementation and Impact
We will implement these ideas in an open-source software toolbox readily applicable to visual recognition and inverse imaging problems, which will also handle other modalities. This initiative will stimulate interdisciplinary collaborations, with the potential to be a game changer in the way scientists and engineers design machine learning problems.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.999.375 |
Totale projectbegroting | € 1.999.375 |
Tijdlijn
Startdatum | 1-9-2023 |
Einddatum | 31-8-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUEpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
Cascade Processes for Sparse Machine LearningThe project aims to democratize deep learning by developing small-scale, resource-efficient models that enhance fairness, robustness, and interpretability through innovative techniques from statistical physics and network science. | ERC Starting... | € 1.499.285 | 2023 | Details |
Scalable Learning for Reproducibility in High-Dimensional Biomedical Signal Processing: A Robust Data Science FrameworkScReeningData aims to develop a scalable learning framework to enhance statistical robustness and reproducibility in high-dimensional data analysis, reducing false positives across scientific domains. | ERC Starting... | € 1.500.000 | 2022 | Details |
AI-based Learning for Physical SimulationThis project aims to enhance physical simulations by integrating machine learning with equation-based modeling for improved generalization and intelligibility, applicable across scientific disciplines and engineering. | ERC Starting... | € 1.315.000 | 2022 | Details |
Control for Deep and Federated LearningCoDeFeL aims to enhance machine learning methods through control theory, developing efficient ResNet architectures and federated learning techniques for applications in digital medicine and recommendations. | ERC Advanced... | € 2.499.224 | 2024 | Details |
Optimizing for Generalization in Machine LearningThis project aims to unravel the mystery of generalization in machine learning by developing novel optimization algorithms to enhance the reliability and applicability of ML in critical domains. | ERC Starting... | € 1.494.375 | 2023 | Details |
Cascade Processes for Sparse Machine Learning
The project aims to democratize deep learning by developing small-scale, resource-efficient models that enhance fairness, robustness, and interpretability through innovative techniques from statistical physics and network science.
Scalable Learning for Reproducibility in High-Dimensional Biomedical Signal Processing: A Robust Data Science Framework
ScReeningData aims to develop a scalable learning framework to enhance statistical robustness and reproducibility in high-dimensional data analysis, reducing false positives across scientific domains.
AI-based Learning for Physical Simulation
This project aims to enhance physical simulations by integrating machine learning with equation-based modeling for improved generalization and intelligibility, applicable across scientific disciplines and engineering.
Control for Deep and Federated Learning
CoDeFeL aims to enhance machine learning methods through control theory, developing efficient ResNet architectures and federated learning techniques for applications in digital medicine and recommendations.
Optimizing for Generalization in Machine Learning
This project aims to unravel the mystery of generalization in machine learning by developing novel optimization algorithms to enhance the reliability and applicability of ML in critical domains.