English(EN) Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery

Seq103框架发现参数更少的紧凑序列架构

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-03 19:01

研究人员开发了Seq103，一个新颖的神经进化框架，旨在发现紧凑序列架构。该统一系统利用共享的进化骨干和可选的循环扩展来处理步进循环和样本前馈序列分类任务。Seq103在各种文本分类和时间序列数据集上，在保留高百分比基线准确性的同时，展示了显著的参数减少。 AI

影响该框架通过在保持性能的同时减少参数数量，可以实现更高效的序列模型开发。

排序理由该集群包含一篇详细介绍新神经网络架构发现框架的学术论文。

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arXiv cs.AI TIER_1 English(EN) · Wenxiao Li, Yongjian Liu, Qing Xie · 2026-06-09 04:00

Seq103：用于紧凑序列架构发现的统一神经进化框架

arXiv:2606.07664v1 Announce Type: cross Abstract: Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms. In this paper, we propose Seq103, a unified NEAT-style neuroevolution framework…
arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Qing Xie · 2026-06-03 19:01

Seq103：用于紧凑序列架构发现的统一神经进化框架

Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms. In this paper, we propose Seq103, a unified NEAT-style neuroevolution framework for compact sequence architecture discovery. Seq1…