Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 19.2 - Hyperbolic Graph Embeddings скачать в хорошем качестве

Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 19.2 - Hyperbolic Graph Embeddings

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Brc7vN Jure Leskovec Computer Science, PhD In previous lectures, we focused on graph representation learning in Euclidean embedding spaces. In this lecture, we introduce hyperbolic embedding spaces, which are great for modeling hierarchical, tree-like graphs. Moreover, we introduce basics for hyperbolic geometry models, which leads to the idea of hyperbolic GNNs. More details can be found in the paper: Hyperbolic Graph Convolutional Neural Networks: https://arxiv.org/abs/1910.12933 To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224w/ 0:00 Introduction 0:19 Hyperbolic Graph Embeddings 2:08 Graph Embedding Geometry 3:04 Hyperbolic Embedding Space 3:50 Hyperbolic Space Model (2) 10:32 Task 12:50 Hyperbolic Geometry (2) 15:20 Hyperbolic Geometry Models 18:12 Geodesic Distance (2) 20:26 Mapping to and from Tangent Space 22:31 Hyperbolic GNN (3) 31:03 Hyperbolic GNN: Summary #machinelearning #machinelearningcourse