13 citations · 33 across the 7 of their papers we have counts for
6 papers · 1 filter
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,…
Diffusion Generative Modeling on Lie Group Representations
Marco Bertolini, Tuan Le, Djork-Arné Clevert
We introduce a novel class of score-based diffusion processes that operate directly in the representation space of Lie groups. Leveraging the framework of Generalized Score Matchin…
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
Tuan Le, Julian Cremer, Frank Noé +2
Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptim…
Equivariant Graph Attention Networks for Molecular Property Prediction
Tuan Le, Frank Noé, Djork-Arné Clevert
Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery. Equivariant Grap…
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…
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Tuan Le, Marco Bertolini, Frank Noé +1
Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs. Motivated by the complex and quater…