动态神经网络结构:对其理论和应用的审查
概括
动态神经网络 (DNN) 与静态网络相比,提供了更高的准确性和效率. 本综述探讨了DNN在深度学习 (DL) 和广泛学习系统 (BLS) 中的进步,详细介绍了构建,推断和应用.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 静态神经网络在准确性,效率和可解释性方面存在局限性.
- 动态神经网络 (DNN) 由于灵活的结构和参数而具有优势.
- 广义学习系统 (BLS) 的演变已经将DNN应用扩展到传统的深度学习 (DL) 之外.
研究的目的:
- 提供一个全面的动态神经网络 (DNN) 结构的综述.
- 在深度学习 (DL) 和广泛学习 (BL) 中探索DNNs的进步.
- 讨论动态构造和推理的技术,应用以及未来的研究方向.
主要方法:
- 文献综述侧重于动态神经网络.
- 对DNN中的动态构造和推理技术的分析.
- 检查不同领域的DNN应用程序.
主要成果:
- 与静态网络相比,DNN在准确性,效率和解释性方面取得了显著的改进.
- 在深度学习 (DL) 和广泛学习 (BL) 领域,都观察到DNN结构的快速发展.
- DNN适用于广泛的领域,目前正在进行的研究解决了一些尚未解决的问题.
结论:
- 动态神经网络比静态模型具有显著的进步.
- 对DNN的进一步研究,特别是在DL和BL中,对未来的创新有希望.
- 本综述对于研究DNN及其应用的研究人员来说是一个有价值的资源.
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