First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper's core finding is that combining an identity classification task as well as metric-learning-style verification task helps to learn better features for face classification/verification. The verification task here tries to decrease feature-space distance between instances of the same identity, and increase distance between those of different identities. This improvement is embedded in a state-of-the-art system for face verification, which uses convnets trained on many (400) different views to generate features, distilled into a small set of 25 using feature selection. Very good results are obtained and experiments performed using LFW as a test set. Overall, these are very good results obtained using a somewhat complex pipeline, and a good investigation into the contribution of each task in the loss for feature learning.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes to learn bilingual word vector representations through an autoencoder. The novelty of this approach is to not rely on word-level alignments. It only requires aligned sentences. An autoencoder model is used to reconstruct the bag-of-words representation of aligned sentences, within and between languages.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors provide finite sample bounds on the excess risk of these classifiers. When taken to the limit these bounds reproduce the known consistency results for this class. However, they are superior in two ways: 1. They apply in the finite case 2. They apply to a broader set of metric spaces The presentation is very clear and the intuition is well described.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The submission describes a convex deep learning formulation that leverages a number of key ideas. First, a training objective is proposed that explicitly includes the outputs of hidden layers as variables to be inferred via optimization. These are linked to linear responses via a loss function, and the net objective is the sum of these loss functions across the layers, plus some regularization terms. Next, a number of changes of variables are performed in order to reparameterize the objective into a convex form, heavily leveraging the representer theorem and the idea of value regularization. We are left with a convex objective in terms of three different matrices (per layer) to optimize. In particular, one of these matrices is a nonparametric'normalized output kernel' matrix, which takes the place of optimizing over the hidden layer outputs directly; however, this leads to a transductive method where we must simultaneously solve the optimization for training and test inputs.

"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1407" "Title:","On Communication Cost of Distributed Statistical Estimation and Dimensionality" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper investigates the communication cost of distributed estimation for d-dimensional spherical Gaussian distribution with unknown mean and unitary covariance, where the joint distribution is assumed to be a product distribution of each coordinate. The authors generalize previous works on the one-dimensional case in [4] by proposing upper and lower bounds for d-dimensional data on two communication schemes, interactive and simultaneous communication settings, for achieving minimax squared loss. The results establish the tradeoffs between dimensionality and communication cost for distributed estimation. In addition, improved bounds are derived when the unknown mean is s-sparse.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper addresses the problem of robustly estimating the low-dimensional subspace of contaminated observations when the observations are inherently coherent. Performance goes worse with increasing data coherence is a standard theoretical bottleneck of previous RPCA methods. This paper, however, circumvents this problem in a clever manner. Considering that such cluster structure is rather common in realistic data, solving this issue is certainly significantly meaningful.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The paper extends the classical BCM learning rule to utilize information from spike triplets. It is shown that the update rule can learn selectivity for mixture distributions (by converging to the class means). Quality: By employing tensor notation, the paper shows that the BCM rule can be generalized to use information from more than a pair of spikes (spike triplets are used for the examples). While the model has fewer parameters than previous learning algorithms based on spike triplets or quadruplets and the method can be shown to have stable points as class means of mixture distributions, the lack of experimental comparisons with other models makes it hard to gauge the incremental contribution of the model.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a new Gibbs sampler algorithm for FHMMs. The idea is to add an auxillary variable, U, to the state of the Gibbs sampler. The value of U restricts the set of possible values that the hidden state X can take at the next step of the Gibbs sampler. As the number of possible values for X_i is small for each time point i, we can update X given U (and the data) using FFBS. I think this is an original and clever approach to an important class of problems.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper connects two previously described methods of multitask feature learning, one where regularization is applied within and across tasks separately, and one where the regularization is performed on the jointly learned parameter matrix. This paper proves that under specific parameter settings, the two formulations are equivalent. This paper is fairly clear, but I would have liked one final statement of the full form of problem 1 using problem 2's parameters (or vice versa) rather than the current stating of parameter equivalence. However, the proofs are nicely presented with sketches of the steps described beforehand to help the reader stitch together the parts.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors present a novel approach to learning to rank. In contrast to traditional approaches, the idea is to focus on the number of positive instances that are ranked before the first negative one. Following a large-margin approach leads to primal and dual representations. Compared to similar approaches, the complexity is only linear in the number of instances.