Graph Neural Networks Programming Tutorials & Engineering Articles

The article discusses the integration of AI Physics into Technology Computer-Aided Design (TCAD) simulations, highlighting its significance in semiconductor manufacturing.

The article discusses the application of Graph Neural Networks (GNNs) in enhancing fraud detection within financial services.

This article discusses the Candidate Generation (CG) stage of LinkedIn's People You May Know (PYMK) recommendation system, detailing the various techniques used to generate relevant candidate pools...

The article discusses the application of Graph Neural Networks (GNNs) in optimizing the design and simulation of lattice structures in additive manufacturing.

This article explores the optimization of memory and retrieval processes for large-scale Graph Neural Networks (GNNs) using WholeGraph, a feature of the RAPIDS cuGraph library.

The article discusses WholeGraph, a feature in the RAPIDS cuGraph library designed to optimize memory and retrieval for Graph Neural Networks (GNNs).

The article discusses the rapid adoption of federated learning (FL) and the new features introduced in NVIDIA FLARE 2. 4.

A Stanford University team is revolutionizing cardiovascular care through AI-driven simulations that provide patient-specific blood flow visualizations.

The article discusses NVIDIA AI Enterprise 4. 0, a comprehensive solution designed to support enterprises in developing and deploying generative AI applications.

This article introduces Graph Neural Networks (GNNs) and how to utilize cuGraph-DGL, a GPU-accelerated library for graph computations.

The article discusses the design of deep neural networks (DNNs) that can process the weights of other DNNs, focusing on architectures that leverage the symmetries of weight spaces.

The article discusses NVIDIA PhysicsNeMo, a framework for developing physics-informed machine learning models, with a focus on the latest update that introduces support for Graph Neural Networks (G...

The NVIDIA Grace Hopper Superchip Architecture represents a significant advancement in heterogeneous computing, combining NVIDIA Grace CPUs and Hopper GPUs to optimize performance for AI and high-p...

The article discusses how Graph Neural Networks (GNNs) and NVIDIA GPUs can optimize fraud detection in financial services.

The article discusses the application of graph machine learning at Airbnb, highlighting how graph structures can enhance machine learning models by providing contextual information about users.

NVIDIA experts will present advancements in robotics, Graph Neural Networks (GNNs), and Natural Language Processing (NLP) at the WeAreDevelopers World Congress in Berlin.

The article discusses the Performance-Adaptive Sampling Strategy (PASS) for Graph Neural Networks (GNNs) and announces its open-source release.

The article discusses the completion of member knowledge graphs using Graph Neural Networks (GNNs), specifically introducing a novel model called Entity-BERT.

NVIDIA has introduced GPU-accelerated Deep Graph Library (DGL) containers to assist developers, researchers, and data scientists in working with Graph Neural Networks (GNN) on large heterogeneous g...

The article discusses the application of relational graph learning, specifically relational graph convolutional networks (RGCN), to detect collusion in fraudulent activities within the Uber platfor...

NVIDIA researchers, in collaboration with Stanford University and Bar Ilan University, received the Outstanding Paper Award at ICML 2020 for their paper 'On Learning Sets of Symmetric Elements'.

MIT researchers, in collaboration with the Qatar Computing Research Institute, have developed an AI model named RoadTagger that enhances digital maps using deep learning.

This article discusses the use of Graph Neural Networks for predicting vehicle trajectories in autonomous driving by modeling pairwise interactions among agents.

The article introduces Graph Recurrent Attention Networks (GRANs), a new family of deep generative models designed for efficient graph generation.

The article discusses the use of Graph Neural Networks (GNNs) for inference in probabilistic graphical models, highlighting their ability to outperform traditional message-passing algorithms like b...

The article discusses NerveNet, a novel approach to learning structured policies for continuous control using Graph Neural Networks.

The article discusses the development of a 3D Graph Neural Network (3DGNN) for RGBD semantic segmentation, emphasizing the integration of 2D appearance and 3D geometric information.

The article discusses the use of Graph Neural Networks (GNNs) for recognizing situations in images by predicting salient verbs and their associated semantic roles.