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- W3172357953 abstract "Variational inference enables approximate posterior inference of the highly over-parameterized neural networks that are popular in modern machine learning. Unfortunately, such posteriors are known to exhibit various pathological behaviors. We prove that as the number of hidden units in a single-layer Bayesian neural network tends to infinity, the function-space posterior mean under mean-field variational inference actually converges to zero, completely ignoring the data. This is in contrast to the true posterior, which converges to a Gaussian process. Our work provides insight into the over-regularization of the KL divergence in variational inference." @default.
- W3172357953 created "2021-06-22" @default.
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- W3172357953 date "2021-06-13" @default.
- W3172357953 modified "2023-09-27" @default.
- W3172357953 title "Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data." @default.
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