GraphCNN + CNN Network for EEG Emotion Recognition
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Updated
May 29, 2024 - Jupyter Notebook
GraphCNN + CNN Network for EEG Emotion Recognition
Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages
Weather prediction on stereo images using a graph equivariant convolutional neural network.
A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs.
A collections of all deep learning experiments we have throughout the deep learning courses
Automated Headline generation and Aspect Based Sentiment Analysis
Code for "Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction" CVPR 2020
Code for HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data, IEEE PerCom CoMoRea 2022
ECE271B: Statistical Learning II Final Project with David Glukhov
Code for: "Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs", ICCV2021 Workshops
A Lightweight Residual Graph CNN for Pedestrians Trajectory Prediction
A TensorFlow 2 implementation of Graph Convolutional Networks (GCN)
Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
Graph convolutional networks for structural learning of proteins
Algorithms for prediction of congestion from Network State
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