Background Introduction The main purpose of Graph Representation Learning (Graph Representation Learning, Network Embedding, Graph Embedding) is to map each node in the graph to a low-dimensional vector space, preserving the original structure and distance information of the graph. Intuitively, two points that are close together in the graph should also be close together in … Read more
This article is reproduced with permission from the WeChat public account Paper Weekly (ID: paperweekly). Paper Weekly shares interesting papers in the field of natural language processing every week. “In-depth Analysis: GAN Models and Their Progress in 2016” [1] provides a detailed introduction to the progress of GANs over the past year, which is highly … Read more
New Intelligence Report Source: towardsdatascience Author: Connor Shorten Editor: Xiao Qin [New Intelligence Guide]Generative Adversarial Networks (GANs) are one of the most fascinating and popular applications in deep learning. This article lists 10 papers on GANs that will provide you with a great introduction to GANs and help you understand the foundations of state-of-the-art techniques. … Read more