论文标题
粒子识别的变化辍学率
Variational Dropout Sparsification for Particle Identification speed-up
论文作者
论文摘要
准确的颗粒识别（PID）是LHCB实验最重要的方面之一。现代的机器学习技术（例如神经网络（NNS））有效地应用于此问题，并将其集成到LHCB软件中。在这项研究中，我们讨论了神经网络加速技术的新应用，以在LHC升级条件下实现更快的PID。我们表明，最佳结果是使用变异辍学率获得的，即使与具有浅网络的模型相比，即使在预测（前馈传中）的速度也达到16倍。
Accurate particle identification (PID) is one of the most important aspects of the LHCb experiment. Modern machine learning techniques such as neural networks (NNs) are efficiently applied to this problem and are integrated into the LHCb software. In this research, we discuss novel applications of neural network speed-up techniques to achieve faster PID in LHC upgrade conditions. We show that the best results are obtained using variational dropout sparsification, which provides a prediction (feedforward pass) speed increase of up to a factor of sixteen even when compared to a model with shallow networks.
