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Noisy Recurrent Neural Networks
[article]
2021
arXiv
pre-print
We provide a general framework for studying recurrent neural networks (RNNs) trained by injecting noise into hidden states. Specifically, we consider RNNs that can be viewed as discretizations of stochastic differential equations driven by input data. This framework allows us to study the implicit regularization effect of general noise injection schemes by deriving an approximate explicit regularizer in the small noise regime. We find that, under reasonable assumptions, this implicit
arXiv:2102.04877v3
fatcat:vfwxcpjc25bfvd7d5gvy7iqa7i