标题： 关于前向-前向算法的进展 显示英文标题

标题： On Advancements of the Forward-Forward Algorithm

摘要： 前向-前向算法在机器学习研究中得到了发展，能够处理更多模拟实际应用的复杂任务。 在近年来，它通过多种技术得到改进，表现优于其原始版本，在处理像CIFAR10这样的挑战性数据集时不会失去其灵活性和低内存使用特性。 我们在结果中表明，通过在训练过程中结合卷积通道分组、学习率调度和独立块结构，实现了改进，使测试错误百分比减少了20%。 此外，为了进一步在低容量硬件项目上实现，我们提出了一系列轻量级模型，在（21$\pm$3）%的测试错误百分比范围内，并且可训练参数数量在164,706到754,386之间。 这为我们未来对这类神经网络的完整验证和确认提供了基础。 显示英文摘要

摘要： The Forward-Forward algorithm has evolved in machine learning research, tackling more complex tasks that mimic real-life applications. In the last years, it has been improved by several techniques to perform better than its original version, handling a challenging dataset like CIFAR10 without losing its flexibility and low memory usage. We have shown in our results that improvements are achieved through a combination of convolutional channel grouping, learning rate schedules, and independent block structures during training that lead to a 20\% decrease in test error percentage. Additionally, to approach further implementations on low-capacity hardware projects, we have presented a series of lighter models that achieve low test error percentages within (21$\pm$3)\% and number of trainable parameters between 164,706 and 754,386. This serves as a basis for our future study on complete verification and validation of these kinds of neural networks.