  In this paper, we introduce the novel concept of densely connected layers
into recurrent neural networks. We evaluate our proposed architecture on the
Penn Treebank language modeling task. We show that we can obtain similar
perplexity scores with six times fewer parameters compared to a standard
stacked 2-layer LSTM model trained with dropout (Zaremba et al. 2014). In
contrast with the current usage of skip connections, we show that densely
connecting only a few stacked layers with skip connections already yields
significant perplexity reductions.
