). This strategy is called latent autoregressive modeling. Contrary to n-grams, it does not require tuning the time span hyperparameter and storing sparse parameters (probabilities), which will likely capture only short contexts and overfit the training dataset. Recurrent neural networks (RNNs) can be viewed as latent autoregressive models. In Section IV-B, we describe SMILESRNN architecture, which was chosen to be the baseline generative language model.