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,
Conversely, graph neural networks (GNNs), which operate on graph data composed of elements with a set of features and their pairwise connections, provide an alternative way of incorporating weight sharing, local connectivity, and specialized domain knowledge. Particle physics data, such as the hits...
Choma, N. et al. Graph neural networks for icecube signal classification. In17th IEEE International Conference on Machine Learning and Applications (ICMLA)386–391 (IEEE, 2018). Shlomi, J., Battaglia, P. & Vlimant, J.-R. Graph neural networks in particle physics.Mach. Learn. Sci. Technol...
Vlimant, Graph Neural Networks in particle physics (2020) arXiv:2007.13681. [42] J. Krupa et al., GPU coprocessors as a service for deep learning inference in high energy physics (2020) arXiv:2007.10359. [43] A. Heintz et al., Accelerated charged particle tracking with graph neural ...
Haggai Maron,英伟达研究科学家,「provably expressive high-dimensional graph neural networks」作者。 图神经网络的表达能力是 2020 年图机器学习领域的核心问题之一。 2020 年,有许多优秀的论文讨论了各种 GNN 架构的表达能力[27],指出了由于深度和宽度的限制,导致 GNN 存在根本上的表达能力局限性[28]。此外,也有...
However, several new-physics models predict a significant enhancement in the HH production rate compared to the Standard Model (SM) prediction, especially when the H pairs are very energetic, or boosted. Recently, the CMS collaboration developed a new strategy employing graph neural networks to ...
Here we demonstrate that a Graph Neural Network (GNN) trained to predict the optical properties of numerically-generated BC fractal aggregates can accurately generalize to arbitrarily shaped particles, including much larger ([Math Processing Error]10×) aggregates than in the training dataset. This ...
We employ graph neural networks (GNN) to analyse and classify physical gel networks obtained from Brownian dynamics simulations of particles with competing attractive and repulsive interactions. Conventionally such gels are characterized by their position in a state diagram spanned by the packing fraction...
More recent advancements in ANNs for structural analysis can be found in Refs. [24], [25], [26], [27]. However, limited by the monolithic structure of traditional neural networks, even if additional layers can be added to increase the complexity of the architecture, MLPs are inefficient at...
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