A method infers influential instances for data cleansing in SGD-trained models by retracing training steps with intermediate models, shown to improve performance on MNIST and CIFAR10.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
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Data Cleansing for Models Trained with SGD
A method infers influential instances for data cleansing in SGD-trained models by retracing training steps with intermediate models, shown to improve performance on MNIST and CIFAR10.