Updated version, draft of accepted IJCAI2020 Survey PaperInternational audienceNeural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several do...
International audienceGraph data is omnipresent and has a wide variety of applications, such as in n...
Recent advances in neural algorithmic reasoning with graph neural networks (GNNs) are propped up by ...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
Updated version, draft of accepted IJCAI2020 Survey PaperInternational audienceNeural-symbolic compu...
The goal of neural-symbolic computation is to integrate ro-bust connectionist learning and sound sym...
Combining machine learning (ML) and computational logic (CL) is hard, mostly because of the inherent...
Combining machine learning (ML) and computational logic (CL) is hard, mostly because of the inherent...
Current advances in Artificial Intelligence and machine learning in general, and deep learning in pa...
Current advances in Artificial Intelligence and machine learning in general, and deep learning in pa...
Neuro-Symbolic Integration is an interdisciplinary area that endeavours to unify neural networks and...
Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling to...
The need for neural-symbolic integration becomes apparent as more complex problems are tackled, and ...
This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (...
In recent years, neural systems have displayed highly effective learning ability and superior percep...
This article describes an approach to combining symbolic and connectionist approaches to machine lea...
International audienceGraph data is omnipresent and has a wide variety of applications, such as in n...
Recent advances in neural algorithmic reasoning with graph neural networks (GNNs) are propped up by ...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
Updated version, draft of accepted IJCAI2020 Survey PaperInternational audienceNeural-symbolic compu...
The goal of neural-symbolic computation is to integrate ro-bust connectionist learning and sound sym...
Combining machine learning (ML) and computational logic (CL) is hard, mostly because of the inherent...
Combining machine learning (ML) and computational logic (CL) is hard, mostly because of the inherent...
Current advances in Artificial Intelligence and machine learning in general, and deep learning in pa...
Current advances in Artificial Intelligence and machine learning in general, and deep learning in pa...
Neuro-Symbolic Integration is an interdisciplinary area that endeavours to unify neural networks and...
Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling to...
The need for neural-symbolic integration becomes apparent as more complex problems are tackled, and ...
This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (...
In recent years, neural systems have displayed highly effective learning ability and superior percep...
This article describes an approach to combining symbolic and connectionist approaches to machine lea...
International audienceGraph data is omnipresent and has a wide variety of applications, such as in n...
Recent advances in neural algorithmic reasoning with graph neural networks (GNNs) are propped up by ...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
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