Tikhonov Regularization for Long Short-Term Memory Networks

9 Aug 2017 Andrei Turkin

It is a well-known fact that adding noise to the input data often improves network performance. While the dropout technique may be a cause of memory loss, when it is applied to recurrent connections, Tikhonov regularization, which can be regarded as the training with additive noise, avoids this issue naturally, though it implies regularizer derivation for different architectures... (read more)

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