research
∙
10/09/2021
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces
Any general artificial intelligence system must be able to interpret, op...
share
research
∙
10/24/2020
Pathfinder Discovery Networks for Neural Message Passing
In this work we propose Pathfinder Discovery Networks (PDNs), a method f...
research
∙
07/06/2020
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
In this work, we examine a novel forecasting approach for COVID-19 case ...
research
∙
07/03/2020
Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
research
∙
10/30/2019
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
We investigate under and overfitting in Generative Adversarial Networks ...
research
∙
05/06/2019
Nostalgin: Extracting 3D City Models from Historical Image Data
What did it feel like to walk through a city from the past? In this work...
research
∙
04/30/2019
MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural ...
research
∙
02/24/2018
