The data in particle physics\nare often represented by sets and graphs and as such, graph neural networks\noffer key advantages. Here we review various applications of graph neural\nnetworks in particle physics, including different graph constructions, model\narchitectures and learning objectives, as...
Graph neural networks in particle physics: implementations, innovations, and challenges. Preprint at https://arxiv.org/abs/2203.12852 (2022). Glashow, S. Partial-symmetries of weak interactions. Nucl. Phys. 22, 579–588 (1961). Article Google Scholar Weinberg, S. A model of leptons. Phys....
在论文「Towards scale-invariant graph-related problem solving by iterative homogeneous graph neural networks」中,作者则提出了一种迭代式的 GNN,可以学会自适应地终止消息传递过程[14]。 在2021 年,我们希望看到研究社区在「条理化的图生成模型方法」,「基于 GNN 的图匹配与 GNN 的表达能力之间的联系」,「学习...
[41] J. Shlomi, P. Battaglia, J.-R. Vlimant,Graph Neural Networks in particle physics(2020) arXiv:2007.13681. [42] J. Krupaet al.,GPU coprocessors as a service for deep learning inference in high energy physics(2020) arXiv:2007.10359. [43] A. Heintzet al.,Accelerated charged particl...
Convolutional Neural Networks on Graphs (by Xavier Bresson): 以Spectral的方法为主，介绍了这个方向的...
13 WAGNER ADAM ZSOLT_ CONSTRUCTIONS IN COMBINATORICS VIA NEURAL NETWORKS 1:20:39 ZSOLT LÁNGI_ AN ISOPERIMETRIC PROBLEM FOR THREE-DIMENSIONAL PARALLELOHEDRA 1:04:27 MOTIVATION AND ALGEBRAIC BACKGROUND - LECTURE 1 1:07:10 PERFECT MATCHINGS IN GRAPHINGS 1:25:31 THE FRACTAL (MANDELBROT) ...
4. Constructing Hybrid Physics–Data Models There are two main steps in physics-guided neural networks (PGNN): (a) creating hybrid models that combine physics-based models and neural networks; these are called hybrid physics data (HPD) models; and (b) using scientific knowledge as physics-based...
Modern Graph Neural Networks ChebNet是graph中学习局部过滤器的一个突破,它激发了许多人从不同的角度思考图卷积。 多项式过滤器对x卷积可以展开为: \begin{aligned} ( L x ) _ { v } & = L _ { v } x \\ & = \sum _ { u \in G } L _ { v u } x _ { u } \\ & = \sum _ {...
In addition to the easy application of existing GNNs, PyG makes it simple to implement custom Graph Neural Networks (seeherefor the accompanying tutorial). For example, this is all it takes to implement theedge convolutional layerfrom Wanget al.: ...
13 WAGNER ADAM ZSOLT_ CONSTRUCTIONS IN COMBINATORICS VIA NEURAL NETWORKS 1:20:39 ZSOLT LÁNGI_ AN ISOPERIMETRIC PROBLEM FOR THREE-DIMENSIONAL PARALLELOHEDRA 1:04:27 MOTIVATION AND ALGEBRAIC BACKGROUND - LECTURE 1 1:07:10 PERFECT MATCHINGS IN GRAPHINGS 1:25:31 THE FRACTAL (MANDELBROT) ...