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The document presents a novel method for feature selection in neural networks using stochastic gates, which allows for the selection of relevant features while learning non-linear functions. This approach improves upon traditional methods like LASSO by incorporating a probabilistic relaxation of the `0 norm, enabling better predictive performance and feature selection consistency. The authors demonstrate the effectiveness of their method through various classification, regression, and survival analysis task

Author

niannian zhang

Language

EN