看arXiv新论文ChurnNet：用优化现代AI精准预测客户流失，模型实证更高效。
arXiv:2606.00169v1 Announce Type: new Abstract: Increased competition and the growing similarity of products and services offered by retailers have lo…
实验对比多种Transformer替代架构，含RNN、TCN等，并附个人代码仓库与优缺点笔记
over the last months I've experimented with many alternatives to transformers, such as one i created github repo about: https://github.com/bggb7781-co…
提出可并行化的记忆循环单元，突破传统RNN序列计算瓶颈，显著提升训练效率
arXiv:2601.09495v3 Announce Type: replace Abstract: With the emergence of massively parallel processing units, parallelization has become a desirable …
固定精度Transformer在描述语言时的简洁性，指数级优于线性时序逻辑和循环神经网络，理论证明其强大表达能力。
arXiv:2510.19315v3 Announce Type: replace-cross Abstract: We study succinctness as a measure of the expressive power of transformers. Succinctness -- …