10 citations · 19 across the 3 of their papers we have counts for
6 papers
Few-Shot Image Classification Along Sparse Graphs
Joseph F Comer, Philip L Jacobson, Heiko Hoffmann
Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data. Here, we present a different perspective for data distributions in…
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…
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash +1
The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark…
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…
Sliced Wasserstein Distance for Learning Gaussian Mixture Models
Soheil Kolouri, Gustavo K. Rohde, Heiko Hoffmann
Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algori…