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cs.LG2026
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,…
cs.LG2021★ 1 cited
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…
cs.LG2017★ 1 cited
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…