First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a method for learning the structure of stochastic And-Or grammars. The paper suggests that this generalizes previous work on structure learning, but it's actually a special case, which makes the problem tractable. This is a reasonable point on its own, and the paper makes a nice contribution, so I wouldn't try to argue that the problem is more general than the structure learning problem faced in NLP. The basic algorithm is sensible and successful when compared against other methods for inducing grammars.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Reaction to the author feedback: I cannot agree with point (3): The work of Siracusa III and Fisher (AISTATS 2009) does *not* assume the data are iid, and it allows the generating stucture vary (even if only in a small set of graphical models). While the present proposed method is, in some sense, even more flexible, I'd find it misleading to claim that the proposed method is the first to address the problem. The proposed method is specifically targeted to scenarios where the data generating graphical model may change relatively frequently. The performance of the method is demonstrated using both simulated and real data.

Questions on whether governments have acted promptly enough, and whether lockdown measures can be lifted soon, have since been central in public discourse. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential for addressing these questions and informing governments on future policy directions.

To solve this, we perform probabilistic reasoning over the depth of neural networks.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presented a method to enable the generation of robust plans with partially specified domain models. The motivation of this research topic is well stated. The main contribution of this work is the formalization of the notion of plan robustness with respect to an incomplete domain model. The paper is clearly written and should the general interest for the broad NIPS audience.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. COMMENTS BASED ON REVIEWER DISCUSSIONS AND AUTHOR REBUTTAL: I agree with the other reviewers that more could be done to constrain the specifics of the cue integration mechanism. However, I believe that if the data set is expanded, allowing the models to be better constrained, then the paper is appropriate and interesting for the NIPS community. I have left my quality score as it was, but I agree with the other reviewers that the paper merits a ``1'' rather than a ``2'' for impact score. ORIGINAL REVIEW: Summary: This paper extends an existing model for the perception of visual speed that uses a Bayesian observer model acting on the activity of independent spatiotemporal frequency channels. Previously, the model accounted for illusions of perceived speed by postulating the Bayes-optimal combination of noisy sensory representations with a prior for slow speeds.

Discrete structures play an important role in applications like program language modeling and software engineering.

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Neural Information Processing Systems http://nips.cc/