In recent years, models based on deep neural networks have achieved remarkable results on numerous tasks. Despite their high prediction accuracy, these models are known for their "black-box" nature, which essentially means that the processes that lead to their predictions are difficult to interpret.
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SurvNet: A backward elimination procedure to enhance variable selection for deep neural networks
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