Apr 27, 2021 · This text is concerned with exposing these regularities through unified geometric principles that can be applied throughout a wide spectrum of applications.

As part of the African Master’s in Machine Intelligence (AMMI), we have delivered a course on Geometric Deep Learing (GDL100), which closely follows the contents of our GDL proto-book.

Oct 26, 2024 · Geometric Deep Learning (GDL) is a burgeoning field within artificial intelligence (AI) that extends the capabilities of traditional deep learning models by incorporating geometric …

Geometric Deep Learning has emerged as a cornerstone of modern data science, providing the mathematical tools necessary to process complex non-Euclidean data structures. This blog …

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of …

Jul 25, 2022 · In this post, rather then getting deep into the technical weeds, we present a very brief introduction to geometric deep learning. We largely follow the excellent recent book by …

Jan 10, 2025 · In this article, we review geometric approaches for uncovering and leveraging structure in data and how an understanding of data geometry can lead to the development of …

Non-geometric models have geometric constraints NLPers may say “no such geometry in language”. But “geometry” is not just about spatial arrangement! It is about constraints: design …

Deep neural networks have achieved great success in the last decade. When designing neural networks to handle the ubiquitous geometric data such as point clouds and graphs, it is critical …

Geometric deep learning (GDL) is an approach that generalizes neural network models to non-Euclidean domains, including networks, trees, molecules, graphs, and manifolds, where data …