深度线性神经网络:每个local-min就是global-min
文献:
[6] P. Baldi and K. Hornik, “Neural networks and principal component analysis:
Learning from examples without local minima,” Neural Netw., vol. 2, no. 1, pp.
53–58, 1989. doi: 10.1016/0893-6080(89)90014-2.
[24] K. Kawaguchi, “Deep learning without poor local minima,” in Proc. Advances
Neural Information Processing Systems, 2016, pp. 586–594.
[44] C. Yun, S. Sra, and A. Jadbabaie, “Global optimality conditions for deep neural
networks,” in Proc. Int. Conf. Learning Representations, 2018.
| global-min |

本文探讨深度线性神经网络中,非线性过参数化网络在特定条件下的全局和局部极小点特性。指出存在次优局部最小值,并通过文献引用和假设分析确保虚假谷的不存在,强调了优化路径的重要性。
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