中场神经网络:在瓦瑟斯坦空间上学习映射
1LPSM, Université Paris Cité, France; FiME, France.
概括
我们开发了新的平均场神经网络,用于涉及概率测量的机器学习任务. 这些网络在解决复杂的平均场问题方面表现出精度和效率.
科学领域:
- 计算数学 计算数学 计算数学
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 中场游戏和控制问题涉及运营商在概率测量和函数之间进行映射.
- 学习这些平均场函数在计算上具有挑战性.
- 现有的方法可能缺乏效率或理论保障.
研究的目的:
- 引入新的神经网络架构来学习平均场函数.
- 为这些拟议模型提供理论保证.
- 使用这些网络开发和评估用于解决时间依赖的平均场问题的算法.
主要方法:
- 提出了两个类的神经网络: bin 密度和圆柱体近似.
- 使用普遍近似定理建立了理论支持.
- 进行数值实验以评估准确性,效率和概括错误.
- 开发了用于解决时间依赖的平均场问题的算法.
主要成果:
- 证明了拟议的中场神经网络的准确性和效率.
- 在各种测试分布中展示了有效的概括.
- 成功地应用了网络在瓦瑟斯坦空间中解决半线性局部微分方程.
结论:
- 开发的平均场神经网络为机器学习在概率测量空间提供了强大的工具.
- 这些网络在理论上提供了合理且实际上有效的方法来解决平均场问题.
- 该研究为游戏理论,控制和部分微分方程的先进应用铺平了道路.
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