Title: Variational recurrent models for representation learning. Abstract: We study the problem of learning representations of sequence data. Recent work has built on variational autoencoders to develop variational recurrent models for generation. Our main goal is not generation but rather representation learning for downstream prediction tasks. Existing variational recurrent models typically use stochastic recurrent connections to model the dependence among neighboring latent variables, while generation assumes independence of generated data per time step given the latent sequence. In contrast, our models assume independence among all latent variables given non-stochastic hidden states, which speeds up inference, while assuming dependence of observations at each time step on all latent variables, which improves representation quality.  In addition, we propose and study extensions for improving downstream performance, including hierarchical auxiliary latent variables and prior updating during training. Experiments show improved performance on several speech and language tasks with different levels of supervision, as well as in a multi-view learning setting.