Google DeepMind and Intrinsic developed AI that uses graph neural networks and reinforcement learning to automate multi-robot ...

Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and flexible tool ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification ...

Machine learning (ML) methods have emerged as an efficient surrogate for high-level electronic structure theory, offering precision and computational efficiency. However, the vast conformational and ...

Deep Neural Networks (DNNs) have become one of the most popular research directions in the field of machine learning and achieved impressive results in multiple tasks. However, understanding the ...

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Specifically, the linear sum assignment problems (LSAPs) are solved by the deep neural networks (DNNs). Since LSAP is a combinatorial optimization problem, it is first decomposed into several ...