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Recurrent Neural Networks (RNNs) have made great achievements for sequential prediction tasks. In practice, the target sequence often follows certain model properties or patterns (e.g., reasonable ranges, consecutive changes, resource constraint, temporal correlations between multiple variables, existence, unusual cases, etc.). However, RNNs cannot guarantee their learned distributions satisfy these properties. It is even more challenging for the prediction of large-scale and complexdblp:conf/nips/MaG0S20 fatcat:mt5uf5ktm5dn5bpaqariufwqei