5 citations · 7 across the 7 of their papers we have counts for
4 papers · 1 filter
Frame-based Equivariant Diffusion Models for 3D Molecular Generation
Mohan Guo, Cong Liu, Patrick Forré
Recent methods for molecular generation face a trade-off: they either enforce strict equivariance with costly architectures or relax it to gain scalability and flexibility. We prop…
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data
Zhipeng He, Chun Ouyang, Lijie Wen +2
Adversarial attacks pose a significant threat to machine learning models by inducing incorrect predictions through imperceptible perturbations to input data. While these attacks ar…
Clifford Group Equivariant Diffusion Models for 3D Molecular Generation
Cong Liu, Sharvaree Vadgama, David Ruhe +2
This paper explores leveraging the Clifford algebra's expressive power for $\E(n)$-equivariant diffusion models. We utilize the geometric products between Clifford multivectors and…
Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks
Cong Liu, David Ruhe, Patrick Forré
Most current deep learning models equivariant to or either consider mostly scalar information such as distances and angles or have a very high computational complexi…