High-Order Graph Convolutional Recurrent Neural Network
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Updated
Sep 12, 2018 - Python
High-Order Graph Convolutional Recurrent Neural Network
Repository for experiments with scattering transforms
GDL-project
code to train a neural network to align pairs of shapes without needing ground truth warps for supervision
Fisher-Bures Adversary Graph Convolutional Networks
LCNN: End-to-End Wireframe Parsing
Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://www.deepgcns.org
Simple task for mixed image-graph data
Source code for CVPR 2018 Oral paper "Surface Networks"
PyTorch implementation of "DeepSphere: a Graph-based Spherical CNN", Defferard et al., 2019.
Geometric Deep Learning bachelor thesis project and notes
Experiments with custom conv layers that summarize, propagate, and leverage information about the spatial geometry of features.
HADA (Hiearachical Adversarial Domain Alignment) for brain graph prediction and classification.
PyTorch reimplementation for "KPConv: Flexible and Deformable Convolution for Point Clouds" https://arxiv.org/abs/1904.08889
Learning Representations via Spectral-Biased Random Walks on Graphs at ICJNN 2020
Deep hypergraph U-Net (HUNet) for brain graph embedding and classification.
📚 A fork of PointNet++ for a study on geometric deep learning
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