Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟭𝟳𝟭 Computer Vision Learning 𝗚𝗿𝗮𝗽𝗵 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 by University of Cambridge, Montréal Institute for Learning Algorithms Follow me for similar post :  🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in ICLR 2018 with over 3997 citations. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/eUk7zkz ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. 🔸By stacking layers in which nodes are able to attend over their neighborhoods’ features, it enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. 🔸In this way, This address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. #computervision #artificialintelligence #data