4 papers
Fast and Geometrically Grounded Lorentz Neural Networks
Robert van der Klis, Ricardo Chávez Torres, Max van Spengler +3
Hyperbolic space is quickly gaining traction as a promising geometry for hierarchical and robust representation learning. A core open challenge is the development of a mathematical…
Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
Yuhui Ding, Thomas Hofmann
Equivariant diffusion models have achieved impressive performance in 3D molecule generation. These models incorporate Euclidean symmetries of 3D molecules by utilizing an SE(3)-equ…
Generalized Interpolating Discrete Diffusion
Dimitri von Rütte, Janis Fluri, Yuhui Ding +3
While state-of-the-art language models achieve impressive results through next-token prediction, they have inherent limitations such as the inability to revise already generated to…
Recurrent Distance Filtering for Graph Representation Learning
Yuhui Ding, Antonio Orvieto, Bobby He +1
Graph neural networks based on iterative one-hop message passing have been shown to struggle in harnessing the information from distant nodes effectively. Conversely, graph transfo…