This paper investigates the double descent phenomenon in two-layer neural networks, focusing on the role of L1 regularization and representation dimensions. It explores an alternative double descent phenomenon, named sparse double descent. The study emphasizes the complex relationship between model complexity, sparsity, and generalization, and suggests further research into more diverse models and datasets. The findings contribute to a deeper understanding of neural network training and optimization.

翻译：本文研究了两层神经网络中的双下降现象，重点关注L1正则化和表示维度的作用。它探讨了一种名为稀疏双下降的替代性双下降现象。研究强调了模型复杂度、稀疏性和泛化性能之间的复杂关系，并建议对更多样化的模型和数据集进行进一步研究。这些发现有助于更深入地理解神经网络的训练与优化过程。