Graph Neural Networks (GNN) are a class of neural networks designed to extract information from graphs. Given an input graph, GNN learns a latent representation for each node such that a node’s ...

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and ...

Recently, explainable recommendation has attracted increasing attentions, which can make the recommender system more transparent and improve user satisfactions by recommending products with useful ...

Other than giving us an appreciation how little difference going eight miles an hour over the speed limit makes, that ETA service is a remarkable invention — and one that takes a hell of a lot of ...

Autonomous Knowledge Unification Equitus AI launches KGNN ('Kajun'): 1st Knowledge Graph Neural Network for dynamic data unification, semantic reasoning & flexible decision-making. To lead is to ...

Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst. "Convolutional neural networks on graphs with fast localized spectral filtering." Advances in Neural Information Processing Systems. 2016 ...

Following is what you need for this book: This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as ...

Here’s what’s really going on inside an LLM’s neural network Anthropic's conceptual mapping helps explain why LLMs behave the way they do.

While DeepMind’s original implementation uses an older TensorFlow 1.0 framework, which lacks compatibility with recent libraries, we adapt their architecture to TensorFlow 2, exploring the newly ...