A Deep Generative Model for Graph Layoutdoi:10.1109/TVCG.2019.2934396Kwan-Liu MaOh-Hyun Kwon
A deep generative model for graph layout. Kwon, Ma https://arxiv.org/pdf/1904.12225.pdf Differentiable perturb-and-parse semi-supervised parsing with a structured variational autoencoder. Corro, Titov https://openreview.net/pdf?id=BJlgNh0qKQ https://github.com/FilippoC/diffdp Variational autoen...
输出:一个合成的时间交互网络\widetilde{G}^{\prime}=\left(\widetilde{V}^{\prime}, \widetilde{E}^{\prime}\right),该网络能准确地捕捉到观测到的时间网络\widetilde{G}的结构和时间特性。 3 MODEL 3.1 A Generic Learning Framework 图4概述了我们提出的框架,它包括四个主要阶段。给定一个由时间边集合(...
6 Graph Layout and High-dimensional Data Visualization 7 Graph Representation Learning Systems 8 Datasets 1 Node Representation Learning 1.1 Unsupervised Node Representation Learning DeepWalk: Online Learning of Social Representations Bryan Perozzi, Rami Al-Rfou, Steven Skiena KDD 2014 Node classification, ...
This paper introduces GraphOptima, a framework for optimizing graph layout and readability metrics. GraphOptima automates parameter selection, layout computation, and readability metric calculation. Rather than providing a single ‘optimal’ solution, the framework generates a range of solutions under ...
13 A Data-Driven Graph Generative Model for Temporal Interaction Networks link:https://scholar.google.com.sg/scholar_url?url=https://par.nsf.gov/servlets/purl/10272483&hl=zh-TW&sa=X&ei=HCmOYrzrJ8nFywSFg47QCw&scisig=AAGBfm08x5PFAPPWh_nl6CoUzkqZBeJ3pg&oi=scholarr Abstract 本文提出了...
1)根据是否利用预先设计的身体模型,则可分为generative(model-based) methods, 和discriminative(model-free) methods 。2)根据模型检测人体关键点的层次( high-level abstraction or low-level pixel evidence ) ,可被分为自顶向下和自底向上两种。 这篇论文与现存其他的论文有所不同的是,其重点总结了近年来HPE领...
For improved estimation of traffic conditions and patterns in urban development planning and management (Scenario 2), TrafficGAN, a deep generative model proposed by Zhang et al. (2020b), captures the underlying patterns of how traffic evolves with changing travel demands and the evolving structure...
Key: Robotic Manipulation, Equivariance, Graph Neural Networks, Reinforcement Learning, Deformable Objects ExpEnv: Rigid insertion, rope manipulation, cloth manipulation with multiple end-effectors M^3PC: Test-time Model Predictive Control using Pretrained Masked Trajectory Model Kehan Wen, Yutong Hu, Yao...
Deep Learning ResourceProgress Article: An overview of gradient descent optimization algorithms✅ Book: Make Your Own Neural Network✅ Fast.ai: Practical Deep Learning for Coder (Part 1)✅ Fast.ai: Practical Deep Learning for Coder (Part 2)⬜ ...