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TAdam: A Robust Stochastic Gradient Optimizer
[article]
2020
arXiv
pre-print
Machine learning algorithms aim to find patterns from observations, which may include some noise, especially in robotics domain. To perform well even with such noise, we expect them to be able to detect outliers and discard them when needed. We therefore propose a new stochastic gradient optimization method, whose robustness is directly built in the algorithm, using the robust student-t distribution as its core idea. Adam, the popular optimization method, is modified with our method and the
arXiv:2003.00179v2
fatcat:h632ernfsra5pd4pajr2tfwtxi