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Since ReLU activations also result in a sparse network, does it have the same "feature selection" property as L1 regularization?

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Is there a theoretical way to determine the best learning rate for gradient descent if the function is a simple known polynomial?

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What are the differences between Inception Score and Fréchet Inception Distance?

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What is 'fairness' in machine learning?

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