5 citations · 5 across the 1 of their papers we have counts for
3 papers · 1 filter
Set Representation Learning with Generalized Sliced-Wasserstein Embeddings
Navid Naderializadeh, Soheil Kolouri, Joseph F. Comer +2
An increasing number of machine learning tasks deal with learning representations from set-structured data. Solutions to these problems involve the composition of permutation-equiv…
Wasserstein Embedding for Graph Learning
Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde +1
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are a…
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Phillip Pope, Soheil Kolouri, Mohammad Rostrami +2
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules. Automatic discovery of FGs will i…