Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, and recommendation systems). In this vein, ...

Context engineering—the art of shaping the data, metadata, and relationships that feed AI—may become the most critical ...

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

Google's new Graph Foundation Model delivers up to 40 times greater precision and has been tested at scale on spam detection.

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and ...

Rats exhibit significant recovery of locomotor function following incomplete spinal cord injuries, albeit with altered gait expression and reduced speed and stepping frequency. These changes likely ...

However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of ...

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Explore the fascinating world of convolutional neural networks (CNNs) and uncover how they’ve revolutionized the field of computer vision and deep learning. Understand the building blocks of CNNs and ...