Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics

arXiv:2402.12535v1 Announce Type: new
Abstract: This study introduces a novel transformer model optimized for large-scale point cloud processing in scientific domains such as high-energy physics (HEP) and astrophysics. Addressing the limitations of graph neural networks and standard transformers, our model integrates local inductive bias and achieves near-linear complexity with hardware-friendly regular operations. One contribution of this work is the quantitative analysis of the error-complexity tradeoff of various sparsification techniques for building efficient transformers. Our findings highlight the superiority of using …

applications arxiv cs.lg energy hashing hep-ex physics transformer type