Graphconvolutional network(GCN):g_\alpha(\mathbf{f})=\alpha \tilde{\mathbf{D}}^{-1 / 2} \tilde{\mathbf{W}} \tilde{\mathbf{D}}^{-1 / 2} \mathbf{f}\tag{6}Diffusion CNN (DCNN):\mathbf{f}_{l, j}^{\mathrm{out}}=\xi\left(w_{l j} \mathbf{P}^j \mathbf{f}_l^{\mat...
Monti, Federico, et al. "Geometric deep learning on graphs and manifolds using mixture model CNNs."arXiv preprint arXiv:1611.08402(2016). 摘要:作者提出课一个统一的框架,这个框架能把传统CNN泛化到非欧空间上。作者还说以前的一些工作是他们这个工作的特例。作者在图片,图结构数据和3D形状分析上都取得了...
Geometric deep learning on graphs and manifolds using mixture model CNNs (MoNet) [CVPR'17] 论文:https://arxiv.org/pdf/1611.08402.pdf 代码:https://github.com/sw-gong/MoNet(非官方) 1. Motivation 本文主要提出了mixture model networks (MoNet),一个将CNN架构泛化到非欧域(如graph, manifold)的通用...
5. Michael Bronstein - Geometric deep learning on graphs - going beyond Euclidea是太疯狂了!来自清华大佬的压迫感!竟然把图神经网络GNN/GCN讲的如此透彻!Graph embedding/GraphSAGE/Graph Network的第54集视频,该合集共计73集,视频收藏或关注UP主,及时了解更多相
Morris et al. Future Directions in Foundations of Graph Machine Learning. ICML 2024 Liu et al. Neural Scaling Laws on Graphs, arxiv 2024 Frey et al. Neural scaling of deep chemical models, Nature Machine Intelligence 2023 关于...
Geometric Deep LearningGrids, Groups, Graphs, Geodesics, and GaugesMichael M. Bronstein, Joan Bruna, Taco Cohen, Petar VeličkovićRead the Proto-Book Read the Book Chapters Read the Blog Watch the Keynotes Watch the ML Street Talk Episode Follow the Lectures Contact the Authors...
从论文Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges,了解一下几何深度学习。 https://geometricdeeplearning.com关于这个主题,研究者甚至建了一个网站。 几何深度学习——Geometric Deep Learning 几何深度学习,从对称性和不变性的角度,尝试对一大类机器学习问题进行统一。
教程主讲人Michael Bronstein在CVPR 2017 曾发表论文《几何深度学习:在图和流形上使用CNN混合模型》Geometric deep learning on graphs and manifolds using mixture model CNNs 作者提出一个统一框架可以将CNN结构推广到非欧几里得域(图和流形)中,并可以学习局部的,平稳的,组合的特定任务特征。作者也表明了先前文献中提...
Geometric deep learning (GDL) is based on neural network architectures that incorporate and process symmetry information. GDL bears promise for molecular modelling applications that rely on molecular representations with different symmetry properties and levels of abstraction. This Review provides a structured...
geometric deep learninggraph constructiongraph sparsificationgraph theorynetwork sciencedata scienceClassification is a classic problem in data analytics and has been approached from many different angles, including machine learning. Traditionally, machine learning methods classify samples based solely on their ...