375 citations · 887 across the 7 of their papers we have counts for
8 papers
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces
Sarah Di, Robin Yu, Amol Kapoor
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
Benedek Rozemberczki, Peter Englert, Amol Kapoor +2
In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model…
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Amol Kapoor, Xue Ben, Luyang Liu +4
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecastin…
Scaling Graph Neural Networks with Approximate PageRank
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi +5
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge - many recently propos…
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
Ben Adlam, Charles Weill, Amol Kapoor
We investigate under and overfitting in Generative Adversarial Networks (GANs), using discriminators unseen by the generator to measure generalization. We find that the model capac…
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor +5
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 mix…
Nostalgin: Extracting 3D City Models from Historical Image Data
Amol Kapoor, Hunter Larco, Raimondas Kiveris
What did it feel like to walk through a city from the past? In this work, we describe Nostalgin (Nostalgia Engine), a method that can faithfully reconstruct cities from historical…
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi +1
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embedding…