2021. Distributed hybrid CPU and GPU training for graph neural networks on billion-scale graphs. Retrieved from arxiv.org/abs/2112.1534. [194]Zhu Xiaojin and Ghahramani Zoubin. 2002. Learning from labeled and unlabeled data with label propagation. Technical Report....
NIPS 2017 (combining a Multi-Graph CNN (MGCNN) and a recurrent neural network (RNN)) (sRGCNN...
A computational method simulating the motion of elements within a multi-element system using a graph neural network (GNN). The method includes converting a molecular dynamics snapshot of the elements into a directed graph comprised of nodes and edges. The method further includes the step of ...
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link PredictionZhaocheng Zhu (Montreal Institute for Learning Algorithms, University of Montreal, University of Montreal) · Zuobai Zhang (Montreal Institute for Learning Algorithms, University of Montreal, University of Montreal) ...
Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all ...
基于历史上的神经图灵机[8]、可微神经计算机[9]等神经执行器(neural executors)的成功,受益于现在广为使用的各种图机器学习工具包,2020 年发表的一些研究工作从理论上探究了神经执行器的缺陷[5,10,11],提出了一些基于 GNN 的强大的新推理架构[12-15],并且在神经推理任务上具有完美的泛化性能[16]。在 2021 年...
molecular dynamics to study materials, this work could have broad applicability as a general framework for understanding the atomic scale dynamics from MD trajectory data. Compared with the Koopman models previously used in biophysics and fluid dynamics, the introduction of graph convolutional neural ...
The graph neural network model, Trans. Neural Networks 20(1):61-80, 2009 (first neural network...
Graph neural networks (GNNs) are a natural extension of common neural network architectures such as convolutional neural networks (CNN) [1], [2], [3] for image classification to graph structured data [4]. For example, recurrent [5], [6], convolutional [4], [7], [8], [9] and spati...
NeuralPLexer通过将生物分子复合物中的多尺度诱导偏差(multi-scale induced bias in biomolecular complexes)与扩散模型相结合,来预测蛋白质-配体复合物的结构。它以分子图作为配体输入,并利用学习到的统计分布生成3D结构。DiffEE 提出了一个基于预训练的蛋白质端到端扩散生成模型。它能够生成具有正确结合位置的多种蛋白...