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The LogSumExp (LSE) (also called RealSoftMax[1] or multivariable softplus) function is a smooth maximum – a smooth approximation to the maximum function, mainly used by machine learning algorithms.[2] It is defined as the logarithm of the sum of the exponentials of the arguments:
- ^ Zhang, Aston; Lipton, Zack; Li, Mu; Smola, Alex. "Dive into Deep Learning, Chapter 3 Exercises". www.d2l.ai. Retrieved 27 June 2020.
- ^ Nielsen, Frank; Sun, Ke (2016). "Guaranteed bounds on the Kullback-Leibler divergence of univariate mixtures using piecewise log-sum-exp inequalities". Entropy. 18 (12): 442. arXiv:1606.05850. Bibcode:2016Entrp..18..442N. doi:10.3390/e18120442. S2CID 17259055.