Graph Mixture Density NetworksFederico ErricaDavide BacciuAlessio MicheliPMLRInternational Conference on Machine Learning
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...
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 ...
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... Graph Convolution Network 理解与实现 汤凯华 GraphGAN...
Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7144–7153. Google Scholar Mao et al., 2023 Mao, W., Xu, C., Zhu, Q., Chen, S...
21. Graph neural networks can be interpreted as the generalization of convolutional neural networks to irregular-shaped graph structures. While other machine learning methods, e.g., convolutional neural networks are at the peak of publication activity, GNNs are still rising exponentially, with hundreds...
Graph neural networks: A review of methods and applications 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 Graph mining The first application is to solve the ...
Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 7144–7153. Pellegrini S, Ess A, Schindler K, et al. You'll never walk alone: ...
Figure 7a shows the evolution of the probability density of the geometrical and topological disorder of the networks until convergence. Note that the ensemble optimal networks lie in a region of the disorder space characterized by higher regularity (lower H1, H2 values) compared to the previous ...