Variance Networks: When Expectation Does Not Meet Your Expectations

ICLR 2019 Kirill NeklyudovDmitry MolchanovArsenii AshukhaDmitry Vetrov

Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging. In this paper, we introduce variance layers, a different kind of stochastic layers... (read more)

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