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今天的主题是：STAR: Synthesis of Tailored Architectures

Summary

This research paper introduces STAR, a novel framework for automated deep learning architecture synthesis. STAR utilizes a hierarchical search space based on linear input-varying systems, numerically encoded as "genomes," which are optimized using gradient-free evolutionary algorithms. The system is evaluated on autoregressive language modeling, demonstrating significant improvements in model quality, size, and inference cache compared to existing Transformer and hybrid models across multiple benchmarks. The paper details the hierarchical search space, genome encoding, evolutionary optimization process, and experimental results showcasing STAR's effectiveness. Finally, the study explores recurring architectural motifs identified during the evolutionary process.

本文提出了STAR，一种用于自动化深度学习架构合成的新型框架。STAR 利用基于线性输入变化系统的分层搜索空间，将其以“基因组”的形式进行数值编码，并通过无梯度的进化算法进行优化。系统在自回归语言建模任务上进行了评估，相较于现有的 Transformer 和混合模型，在多个基准测试中显著提升了模型质量、规模和推理缓存性能。论文详细介绍了分层搜索空间、基因组编码、进化优化过程以及实验结果，展示了 STAR 的高效性。最后，研究还探讨了进化过程中识别出的重复架构模式。

原文链接：https://arxiv.org/abs/2411.17800