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Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

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In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

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Elsevier Science Sa2024