让神经网络在训练中自动增删结构，更灵活地处理时间序列数据。
Data Classification with Dynamically Growing and Shrinking Neural Networks
- 用蒙特卡洛树搜索动态决定网络结构的增减
- 在多变量时间序列分类上表现优异，准确率显著提升
- 适合需要自适应模型结构的复杂数据任务
数据驱动的神经网络模型构建是人工智能领域的核心问题。传统方法采用固定架构并训练权重，而更先进的思路是同时优化权重与网络结构。本文提出一种新方法，可在训练过程中动态调整网络结构。通过蒙特卡洛树搜索模拟不同架构变化的效果，智能选择最优扩展或收缩策略。该方法在视觉模式和多变量时间序列分类任务中均取得良好效果，尤其在多变量时间序列上表现突出，得益于其可对每个时间序列独立调整结构的能力。实验验证了方法的鲁棒性与适应性，配套提供可复现的Python代码。
原文摘要 · Abstract (English)
The issue of data-driven neural network model construction is one of the core problems in the domain of Artificial Intelligence. A standard approach assumes a fixed architecture with trainable weights. A conceptually more advanced assumption is that we not only train the weights, but also find out the optimal model architecture. We present a new method that realizes just that. This article is an extended version of our conference paper titled "Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search [26]". In the paper, we show in detail how to create a neural network with a procedure that allows dynamic shrinking and growing of the model while it is being trained. The decision-making mechanism for the architectural design is governed by a Monte Carlo tree search procedure which simulates network behavior and allows to compare several candidate architecture changes to choose the best one. The proposed method was validated using both visual and time series datasets, demonstrating its particular effectiveness in multivariate time series classification. This is attributed to the architecture's ability to adapt dynamically, allowing independent modifications for each time series. The approach is supplemented by Python source code for reproducibility. Experimental evaluations in visual pattern and multivariate time series classification tasks revealed highly promising performance, underscoring the method's robustness and adaptability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。