First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper propose to use CNN to classify rhythms from EEG recordings. A dataset with 13 subjects is analyzed. Temporal and spatiotemporal (STFT) data representation are investigated. The paper is well written with a good review of the relevant literature.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. First, apologies for the brevity of this review-I had written a version with more detailed comments and had found a few typos, but can't find it now. This paper is an application of DNNs to a novel area (finding mathematical identities) and my feeling is that while the ideas are novel, this seems like preliminary work that maybe doesn't quite rise to the level of a NIPS publication. Specifically, your method doesn't seem to show particularly impressive performance in discovering identities, even though you only gave it a very limited set of inputs. The expressions that you were finding identities on were from such a limited set that it would probably have been easier to just work out some mathematical rule to discover them rather than applying DNNs; it seems like using a sledgehammer to crack a nut.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper present a heuristic for node selection in Branch and Bound for Mixed Integer Programs based on machine learning. Although machine learning used for node selection is not new the paper present a new approach (to the best of my knowledge). They utilize a classifier together with an oracle for training two aspects: a node selection policy and a node pruning policy. The first one is used to enforce a linear order/priority on the current open nodes of the Branch and Bound while the second one is used to further shrink the list of open nodes by pruning the unpromising ones.

Specifically, label propagation is adopted to instantiate the labeling confidence of partial label examples.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The manuscript describes a very interesting model for the analysis of brain states for multi-region LFP time-series. The time-series are separated in different time-windows. An infinite mixture of Gaussian Processes is considered to model the observations in each window. Brain states are assigned to each observation by means of an underlying HDP and brain regions are assigned to clusters by means of a HDP.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Authors propose a method of estimating a graphical model for continuous data that blends the following three, established ideas: 1) assume the data follows a multivariate Gaussian and estimate using the graphical lasso; 2) do not assume the data follows a multivariate Gaussian and instead use a Gaussian copula, the nonparanormal, to allow arbitrary single variable marginals; or 3) assume a specific tree-structured factorization and model arbitrary bivariate marginals along the tree structure. The proposed method introduces the blossom tree, which is a specific factorization of the model into a collection of densely connected blossom components that are connected by a specific set of tree edges. In particular, each blossom is connected (via a pedicel node) to at most one tree edge. The blossom components are modeled as sparse multivariate Gaussians (or using the non-paranormal copula) and the tree edges are modeled as arbitrary bivariate distributions with single variable marginals that are consistent with the marginal of any blossom pedicel to which they are attached.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents a novel approach for column sampling when the data point clusters comprise of non-convex hulls. Column sampling is important in selecting a small subset of data that represents the properties of the original dataset. The presented approach is based upon the computation of Zeta hulls. The authors model the graph cycles by means of the sum-product rule and integrate them using the Zeta function. The authors set up the optimization problem as finding the subset of points with the strongest point extremenesses.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper combines evidence-based clustering with an importance sampling approach. It further optimizes the weight function calculation of importance sampling, by using a second sampler to estimate the number of true groundings. I recommend to accept this paper because of the importance of the evidence problem, and the fact that the proposed solution is very general and scalable. The paper is well-written and clear, except for Figure 1 (b) and Equations 5,6, and 7, which are poorly explained.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. An attentional mechanism is added to a feedforward convolutional network, with recurrence introduced by the feedback of succeeding attentional focus locations. The system is trained using RL, due to issues of differentiability making the performance gradient unavailable. The system achieves competitive performance, with a dramatic reduction in computational burden. This work tackles an important problem, and makes good progress.