Paper:
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep dynamic generative models are developed to learn sequential dependencies in time-series data. The multi-layered model is designed by constructing a hierarchy of temporal sigmoid belief networks (TSBNs), defined as a sequential stack of sigmoid belief networks (SBNs). Each SBN has a contextual hidden state, inherited from the previous SBNs in the sequence, and is used to regulate its hidden bias. Scalable learning and inference algorithms are derived by introducing a recognition model that yields fast sampling from the variational posterior. This recognition model is trained jointly with the generative model, by maximizing its variational lower bound on the log-likelihood. Experimental results on bouncing balls, polyphonic music, motion capture, and text streams show that the proposed approach achieves state-of-the-art predictive performance, and has the capacity to synthesize various sequences.

Review:
I enjoyed reading the paper. It presents some interesting applications of stochastic variational inference which distinguish it from the usual paper. Methodologically, the paper defines a recurrent sigmoid belief network to model sequential data. Since maximum likelihood learning of the model is difficult, the authors propose to use a variational setup, where the approximating distribution q is defined as another directed model. A lower bound on the log likelihood of data is maximized by alternatively maximizing the joint distribution p(v,h) under samples h from q, and maximizing parameters of q in equation 11 (which I believe is equivalent to minimizing the KL between posterior of p and q, although its never stated that way). The optimization is done with Stochastic Gradient Descent on Monte Carlo samples (as was popularized in Stochastic Variational Inference by Hoffman, Blei et. al and other papers predating NVIL) since exact analytical treatment is not possible.The results are presented on four different datasets with interesting results. With regards to the results, I was surprised that the TSBN performed quite poorly on bouncing balls dataset with an order of 1 (pred err 13.23), but so well with order of 4 (pred err = 3.07). To me this implies that the model has not done a very good job of learning long range structure, through the hidden units. No results are presented for the deep TSBM with order 1. And yet these are compared to the temporal restricted boltzmann machine, which I believe used order 1. Either ways, it would seem that RTRBM actually performs better on bouncing balls than this model if order 1 is used. For Motion Capture results it would have been good to also report results from conditional RBM models (although, I believe the MSE results are worse than those in table 2). The conditional RBMs models videos seem a lot less jittery than the ones attached in supplementary methods for this paper. The presentation of section 5.4 [Prediction] is a little incomplete. It isn't stated what the modeling task is clearly, or what the data is that is modeled at each time step. Presumably its the word distribution at each time step, and this information is available in the supplied reference [31]. However it would be good to have at least a one line description here, space permitting. Similarly for the Dynamic Topic Modeling subsection.   Quality: Paper is well presented, and methods are demonstrated on datasets with quite different characteristics, showing that the models is versatile enough. Clarity: Quite clear presentation of ideas. I would suggest one thing. Wrt equation 7, authors should state that this procedure optimizes the exact bound if q is exactly the posterior of p. Also, it should be stated that optimizing equation 11 is equivalent to minimizing the KL of posterior of p from q. For clarity, a one line note with equation 11 can be added to state that sum_h q(h) d/dtheta log q(h) is 0 and drops out, in equation 11.Originality: The novelty of the paper is in defining the temporal SBM and pairing it with the recognition network. The other novelty is in the application to the different datasets. The methods themselves aren't entirely new, but are an application of neural stochastic variational inference to this model. However, the paper is novel enough, in my opinion, since it shows this method can learn. Significance: The paper extends the range of successful applications to which stochastic variational inference ideas have been applied, and is hence useful.Nevertheless, it would have been useful to compare with other variational idea that do sequence learning (Learning Stochastic Recurrent Networks by Bayer and Oserndorfer).