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Rand4TrustPool

Financiado

Random sampling for trustworthy pooling layers in graph neural networks

Graph neural networks (GNNs) are deep learning architectures that hold the promise of durably changing our way of, e.g., predicting inter-molecular chemical affinity to accelerate drug discovery or smartly balancing power loads on... ver más

05/01/2027

NORSK POLARINSTITU...

227K€

Presupuesto del proyecto: 227K€

Líder del proyecto

NORSK POLARINSTITUTT No se ha especificado una descripción o un objeto social para esta compañía.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-04-25

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023

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NORSK POLARINSTITUTT

226.75K€ | Lider

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 32 meses Fecha Inicio: 2024-04-25
Fecha Fin: 2027-01-05

Líder del proyecto

NORSK POLARINSTITUTT

Presupuesto del proyecto 227K€

Descripción del proyecto Graph neural networks (GNNs) are deep learning architectures that hold the promise of durably changing our way of, e.g., predicting inter-molecular chemical affinity to accelerate drug discovery or smartly balancing power loads on the electricity grid. My project focuses on pooling layers, a major building block of neural networks, including GNNs. In a nutshell, pooling layers generate local summaries of the data to enable faster computations and generate discriminative multiscale representations. The traditional periodic-sampling-based pooling that has proven so successful for data defined on regular grids such as time series or images becomes ill-defined when the underlying space of the data loses its regularity – such as data defined over graphs. Today’s existing solutions stem from a trial-and-error approach often driven by two practical and somewhat short-sighted objectives: computation efficiency and empirical predictive performance. The aim of my project is to add trustworthiness as a main objective in the design of novel pooling layers for GNNs. To achieve this, I will bridge the fields of signal processing, point processes, and random numerical linear algebra with graph machine learning to incorporate random methods (mainly random sampling and random projections) into the design of pooling layers for GNNs. Indeed, randomized approaches are provably well adapted to three pillars of trustworthiness: technical robustness, transparency, and privacy. The outcome of my project will be an extensively tested, theoretically-grounded, trustworthy, ready-to-be-deployed, open-source, GNN-based toolbox, along with its user guide for practitioners. It will be the first GNN architecture benefiting from the theoretical guarantees random pooling layers come with. Such trustworthy GNNs will have an impact in the increasingly many fields in which GNNs are pushing the state-of-the-art: including circuit chip design, physics, drug discovery and network neuroscience.

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