CGBridge is a novel framework designed to enhance the code understanding capabilities of Large Language Models (LLMs) by integrating rich structural information from code graphs. Our approach follows ...

Graph Neural Networks (GNNs) are reshaping AI by enhancing data interpretation and improving applications. Learn how GNNs are crucial in advancing machine learning models. Graph Neural Networks (GNNs) ...

Hausdorff's math unveils the beauty in distance and dimension, a world of self-similar wonder. Hausdorff's math unveils the beauty in distance and dimension, a world of self-similar wonder.

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

Abstract: ResidualGNNPlus is a residue-level Graph Neural Network that reconstructs 39 structural and biochemical features-such as hydrophobicity, SASA, polarity, Secondary Structure data, and ...

Abstract: Effectively capturing complex point cloud information is essential for advanced functionalities in consumer electronics, such as augmented reality, virtual simulations, and 3D printing.

Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

In this video, we will understand what is Convolution Operation in CNN. Convolution Operation is the heart of Convolutional Neural Network. It is responsible for detecting the edges or features of the ...

To stay visible in AI search, your content must be machine-readable. Schema markup and knowledge graphs help you define what your brand is known for. New AI platforms, powered by generative ...