7 citations · 11 across the 5 of their papers we have counts for
5 papers
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
Kumar Shubham, Pavan Karjol, Kiran M K +1
The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that,…
Unsupervised Learning of Group Invariant and Equivariant Representations
Robin Winter, Marco Bertolini, Tuan Le +2
Equivariant neural networks, whose hidden features transform according to representations of a group G acting on the data, exhibit training efficiency and an improved generalisatio…
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Robin Winter, Frank Noé, Djork-Arné Clevert
Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, su…
Auto-Encoding Molecular Conformations
Robin Winter, Frank Noé, Djork-Arné Clevert
In this work we introduce an Autoencoder for molecular conformations. Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformatio…
IVE-GAN: Invariant Encoding Generative Adversarial Networks
Robin Winter, Djork-Arné Clevert
Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation proces…