Small-variance asymptotics for Bayesian neural networks
Bayesian neural networks (BNNs) are a rich and flexible class of models that have several advantages over standard feedforward networks, but are typically expensive to train on large-scale data. In this thesis, we explore the use of small-variance asymptotics-an approach to yielding fast algorithms...
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| Language: | en_US |
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2018
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| Online Access: | https://hdl.handle.net/2144/30745 |