据我们所知,这是第一个可以学习任意输入图条件下的多模态输出分布的DGN。 Graph Mixture Density Networks 所考虑的任务是一个有监督的条件密度估计(CDE)问题,目标是学习条件分布 是与数据集D中的输入图g关联的连续目标标签,假设目标分布为多峰分布,因此,由于上述平均效应,当前DGN无法很好地模拟目标分布。因此,我们...
Graph Mixture Density NetworksFederico ErricaDavide BacciuAlessio MicheliPMLRInternational Conference on Machine Learning
12. Scalable Spatiotemporal Graph Neural Networks 13. Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling 14. c-NTPP: Learning Cluster-Aware Neural Temporal Point Process 15. Trafformer: Unify Time and Space in Traffc Prediction 16. Spatio-Temporal Meta-Graph Learning for ...
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 ...
Figure 5. Discovery of cell-type-specific molecular networks and significant ligand-receptor interactions in Alzheimer’s disease (AD) using autoCell (A) UMAP visualization of subclusters of astrocytes (including disease-associated astrocyte [DAA]), microglia, and OPCs, labeled with autoCell clusters...
such as mixture models47or transformation models48, have also been proposed. In this work, we make use ofsemi-structured deep distributional regression(SDDR,49). SDDR combines neural networks and structured additive distributional regression by embedding the statistical regression model in the neural ne...
Graph neural networks: A review of methods and applications Jie Zhou, ... Maosong Sun, in AI Open, 2020 8.1 Structural scenarios In the following subsections, we will introduce GNNs’ applications in structural scenarios, where the data are naturally performed in the graph structure. 8.1.1 Gr...
Graph Neural Networks for Recommender Systems 图神经网络在推荐系统中的应用。 主要方式: 1)没有额外信息,对user-item interactions构成的graph使用。 2)加入knowledge graph,对knowledge graph使用。 3)加入user social network… 时雨苍剑发表于推荐系统文... GraphGAN: Graph Representation Learning with GAN Xu...
(single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell–cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. ...
Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in low solvus high-re