标题： 通过进一步实验探索穿孔反向传播的性能 显示英文标题

标题： Exploring the Performance of Perforated Backpropagation through Further Experiments

摘要： 穿孔反向传播是一种基于现代对生物神经元树突计算重要性理解的神经网络优化技术。 本文探讨了来自原始发表内容的进一步实验，这些实验来源于2025年2月在卡内基梅隆斯瓦茨中心举办的一次黑客马拉松活动。 学生和当地的匹兹堡机器学习从业者被召集在一起，尝试在他们用于项目的数据集和模型上使用穿孔反向传播算法。 结果显示，该系统可以增强他们的项目，在不影响准确率的情况下最多可实现90%的模型压缩，或者使他们原有模型的准确率提升多达16%。 显示英文摘要

摘要： Perforated Backpropagation is a neural network optimization technique based on modern understanding of the computational importance of dendrites within biological neurons. This paper explores further experiments from the original publication, generated from a hackathon held at the Carnegie Mellon Swartz Center in February 2025. Students and local Pittsburgh ML practitioners were brought together to experiment with the Perforated Backpropagation algorithm on the datasets and models which they were using for their projects. Results showed that the system could enhance their projects, with up to 90% model compression without negative impact on accuracy, or up to 16% increased accuracy of their original models.