First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents a VB method for learning nonlinear state-space models using sparse GPs to model the nonlinear state transition and observation mappings. The proposed method looks very good and efficient, but the empirical evaluation is relatively weak. Quality: The paper appears technically sound, save one minor problem listed below. The method is based on existing solid principles with VB-based sparse GPs, stochastic variational inference and sequential Monte Carlo.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. In particle filtering, the resampling step is a synchronous operation: one needs all the particles before computing the normalised weights (since the denominator is the sum of all the weights), and then resample. The reviewed paper propose an asynchronous resampling mechanism, where the number of children of particle k depends only the weights of particles 1 to k. The proposed idea is quite straightforward, but still interesting and potentially very useful. What is a bit lacking in the current version is some motivation for an asynchronous implementation of particle filtering.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes projecting the parameters of an MRF onto the set of fast-mixing parameters: parameters for which MCMC quickly converges to the true distribution. The authors introduce a Euclidean projection operator that implements this property, but note that it can be difficult to apply. They then smooth it by requiring the projection to be close to an additional matrix input. This is sufficient for many cases, but can be applied repeatedly when the true Euclidean projection is required.

"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1350" "Title:","Distributed Balanced Clustering via Mapping Coresets" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes coreset approach for balanced clustering. The paper is not clearly written and lacks intuition and motivation. Although it refers to clustering, what is exactly the clustering objective function? The problem defined in Sec 4 is slightly modified k-median problem, what does it has to do with cluster balance?

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors propose a novel method for image representation called Convolutional Kernel Methods. It is different from similar methods in that it is not designed for explicitly reconstructing then data, or for classifying it. The patch-map and gradient-map approaches obtain quite competitive numbers on MNIST, and reasonable numbers (if not quite state of the art) on CIFAR-10 and STL-10. The Gabor filters obtained on the natural image patches are quite interesting too.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents an algorithm that achieves optimal regret for sellers in posted-price auctions with strategic buyers. The intuition behind the definition of Regret is not clear enough, what does a small regret mean for the seller. There should be more elaboration on the intuition. The paper is well-written with proofs and theorems clearly stated.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors derive a new convex relaxation for the noisy seriation problem (a combinatorial ordering problem, where variables must be ordered on a line such that their pairwise similarities decrease with their distance on this line). Specifically, they use the construction in Goemans [1] based on sorting networks, in order to optimize over the convex set of permutation vectors (ie. the permutahedron) instead of the convex hull of permutation matrices (ie. the Birkhoff polytope). The new representation reduces the number of constraints from Theta(n^2) to Theta(nlog^2n) and turns out to be in practice significantly faster to solve some instances of the seriation problem. I think this paper provides a very appealing convex relaxation to the seriation problem, since it enables to solve much larger instances (up to several thousands with a standard interior point solver, against to a few hundreds with previous relaxation in [2]).

Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This manuscript presents a flexible discrete latent-state model for population neural data. Approximations (variational and eq 10,11) are necessary to do inference in powerful flexible model. This is a tool for confirmatory analysis; one major weakness as a explorative tool is the necessity to set up the state hierarchy in advance. Originality: It is a novel approach.