Overcoming the vanishing gradient problem in plain recurrent networks

ICLR 2018 Yuhuang HuAdrian HuberJithendar AnumulaShih-Chii Liu

Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deliver promising results in many sequence learning tasks through sophisticated network designs. This paper shows how we can address this problem in a plain recurrent network by analyzing the gating mechanisms in GNNs... (read more)

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