Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics
<|reference_start|>Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics: 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 locality-sensitive hashing (LSH), especially OR & AND-construction LSH, in kernel approximation for large-scale point cloud data with local inductive bias. Based on this finding, we propose LSH-based Efficient Point Transformer (HEPT), which combines E$^2$LSH with OR & AND constructions and is built upon regular computations. HEPT demonstrates remarkable performance on two critical yet time-consuming HEP tasks, significantly outperforming existing GNNs and transformers in accuracy and computational speed, marking a significant advancement in geometric deep learning and large-scale scientific data processing. Our code is available at https://github.com/Graph-COM/HEPT.<|reference_end|>
@article{miao2024locality-sensitive,
    title={Locality-Sensitive Hashing-Based Efficient Point Transformer with
  Applications in High-Energy Physics},
    author={Siqi Miao, Zhiyuan Lu, Mia Liu, Javier Duarte, Pan Li},
    journal={arXiv preprint arXiv:2402.12535},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12535},
    primaryClass={cs.LG hep-ex}
}