Journal Article10.1109/JAS.2021.1004201
Variational Gridded Graph Convolution Network for Node Classification
19
TL;DR: In this paper, a hierarchical-coarsened random walk (hcr-walk) is proposed to encode non-regular graph data, which greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version, while preserving graph structures well.
read more
Abstract: The existing graph convolution methods usually suffer high computational burdens, large memory requirements, and intractable batch-processing. In this paper, we propose a high-efficient variational gridded graph convolution network (VG-GCN) to encode non-regular graph data, which overcomes all these aforementioned problems. To capture graph topology structures efficiently, in the proposed framework, we propose a hierarchically-coarsened random walk (hcr-walk) by taking advantage of the classic random walk and node/edge encapsulation. The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version, while preserving graph structures well. To efficiently encode local hcr-walk around one reference node, we project hcr-walk into an ordered space to form image-like grid data, which favors those conventional convolution networks. Instead of the direct 2-D convolution filtering, a variational convolution block (VCB) is designed to model the distribution of the random-sampling hcr-walk inspired by the well-formulated variational inference. We experimentally validate the efficiency and effectiveness of our proposed VG-GCN, which has high computation speed, and the comparable or even better performance when compared with baseline GCNs.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition
TL;DR: Wang et al. as discussed by the authors proposed a residual graph convolutional broad network (Residual GCB-net), which promoted the performance on a deeper layer network and extracted higher-level information.
42
Visuals to Text: A Comprehensive Review on Automatic Image Captioning
TL;DR: Image captioning refers to automatic generation of descriptive texts according to the visual content of images as mentioned in this paper , which is a technique integrating multiple disciplines including the computer vision (CV), natural language processing (NLP) and artificial intelligence.
31
Dual Feature Interaction-Based Graph Convolutional Network
TL;DR: In this article , a dual feature interaction-based graph convolutional network (DFI-GCN) is proposed to capture the pairwise interactions among nodes in the neighborhood to expand a weighted sum operation.
27
MNL: A Highly-Efficient Model for Large-scale Dynamic Weighted Directed Network Representation
Minzhi Chen,Chun He,Xin Luo +2 more
TL;DR: In this article , a Momentum-incorporated Biased Non-negative and Adaptive Latent Factorization-of-tensors (MNL) model is proposed, which incorporates a generalized momentum method into the NMU-IT algorithm to enable fast model convergence.
17
A Progressive Quadric Graph Convolutional Network for 3D Human Mesh Recovery
TL;DR: Zhang et al. as discussed by the authors applied quadric-based surface simplification to human meshes and designed a progressive graph convolution network, accompanied by mesh feature up-sampling, to deal with the mesh topologies.
15
References
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
- 01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
53.5K
•Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon,Santosh K. Divvala,Ross Girshick,Ali Farhadi +3 more
- 27 Jun 2016
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
•Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
- 01 Jan 2014
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.