Abstract
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of E(p,q)-equivariant CNNs. CS-CNNs process multivector fields on pseudo-Euclidean spaces Rp,q. They specialize, for instance, to E(3)-equivariance on R3 and Poincaré-equivariance on Minkowski spacetime R1,3. Our approach is based on an implicit parametrization of O(p,q)-steerable kernels via Clifford group equivariant neural networks. We significantly and consistently outperform baseline methods on fluid dynamics as well as relativistic electrodynamics forecasting tasks.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Machine Learning (ICML 2024) |
| Number of pages | 28 |
| Publication status | Published - 2024 |
Fields of science
- 305907 Medical statistics
- 202017 Embedded systems
- 202036 Sensor systems
- 101004 Biomathematics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
- 101017 Game theory
- 101018 Statistics
- 101019 Stochastics
- 101024 Probability theory
- 101026 Time series analysis
- 101027 Dynamical systems
- 101028 Mathematical modelling
- 101029 Mathematical statistics
- 101031 Approximation theory
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102004 Bioinformatics
- 102013 Human-computer interaction
- 102018 Artificial neural networks
- 102019 Machine learning
- 102032 Computational intelligence
- 102033 Data mining
- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 202035 Robotics
- 202037 Signal processing
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
JKU Focus areas
- Digital Transformation
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