This thesis delves into the intricate world of Deep Neural Networks (DNNs), focusing on the exciting concept of the Lottery Ticket Hypothesis (LTH). The LTH posits that within extensive DNNs, smaller, trainable subnetworks termed "winning tickets", can achieve performance comparable to the full model. A key process in LTH, Iterative Magnitude Pruning (IMP), incrementally eliminates minimal weights, emulating stepwise learning in DNNs. Once we identify these winning tickets, we further investigate their "universality". In other words, we check if a winning ticket that works well for one specific problem could also work well for other, similar problems. We also bridge the divide between the IMP and the Renormalisation Group (RG) theory in physics, promoting a more rigorous understanding of IMP.

翻译：本论文深入探讨深度神经网络的复杂领域，聚焦彩票假说这一激动人心的概念。彩票假说指出，在大型深度神经网络中，存在更小的可训练子网络（称为"中奖票"），其性能可与完整模型相媲美。彩票假说中的关键过程——迭代幅度剪枝通过逐步剔除最小权值，模拟深度神经网络的分步学习过程。在识别这些中奖票后，我们进一步研究其"普适性"，即检验在特定问题上表现优异的中奖票是否也能适用于其他相似问题。同时，我们建立了迭代幅度剪枝与物理学中重正化群理论之间的桥梁，促进对迭代幅度剪枝更严格的理解。