Search Results for author:
Found 9 papers, 5 papers with code
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces
Any general artificial intelligence system must be able to interpret, operate on, and produce data in a multi-modal latent space that can represent audio, imagery, text, and more.
Pathfinder Discovery Networks for Neural Message Passing
Additional results from a challenging suite of node classification experiments show how PDNs can learn a wider class of functions than existing baselines.
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data.
Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks.
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
We investigate under and overfitting in Generative Adversarial Networks (GANs), using discriminators unseen by the generator to measure generalization.
Nostalgin: Extracting 3D City Models from Historical Image Data
What did it feel like to walk through a city from the past?
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships.
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.
Ranked #43 on
Node Classification
on Pubmed
Network of Graph Convolutional Networks Trained on Random Walks
Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data.
