3 citations · 4 across the 2 of their papers we have counts for
4 papers
Random projection tree similarity metric for SpectralNet
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with -nn graphs, which are usually constr…
The Effect of Points Dispersion on the -nn Search in Random Projection Forests
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
Partitioning trees are efficient data structures for -nearest neighbor search. Machine learning libraries commonly use a special type of partitioning trees called d-trees to…
Random Projection Forest Initialization for Graph Convolutional Networks
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
Graph convolutional networks (GCNs) were a great step towards extending deep learning to unstructured data such as graphs. But GCNs still need a constructed graph to work with. To…
Graph Construction using Principal Axis Trees for Simple Graph Convolution
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
Graph Neural Networks (GNNs) are increasingly becoming the favorite method for graph learning. They exploit the semi-supervised nature of deep learning, and they bypass computation…