3 papers
cs.LG2024
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
Davide Buffelli, Farzin Soleymani, Bastian Rieck
Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neu…
cs.CV2021
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images
Farzin Soleymani, Mohammad Eslami, Tobias Elze +2
We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images. DVC simultaneously learns the multiv…
cs.CV2021
Deep Bregman Divergence for Contrastive Learning of Visual Representations
Mina Rezaei, Farzin Soleymani, Bernd Bischl +1
Deep Bregman divergence measures divergence of data points using neural networks which is beyond Euclidean distance and capable of capturing divergence over distributions. In this…