Author Rank google
Author Rank is a new aspect of Google’s search algorithm that will score online content creators. Similar to SEO rankings for sites and pages, authors will now have an associated ranking based on a few contributing factors, including, but not limited to:
· Social sharing of your Google+ posts
· Quality of backlinks to your content
· Interactions with your content (comments and shares)
· Timely and topical content
· Reputation and authority on other social networks
· PageRank
In short, Google will be assessing your reputation, authority, and the general reception of your content to determine just how valuable you are as an author. This ranking methodology provides writers with a greater incentive to not only build out their Google Plus profiles (smart move, Google), but also to ensure their online presence is streamlined and connected across all social networks and blogs to which they contribute.
A user who is well-connected, well-informed, produces great content, and is seen by the larger community as valuable, will without question reap the rewards of a high author ranking.
A Guide on Google’s Author Rank

Primal-Dual Active-Set (PDAS) google
Isotonic regression (IR) is a non-parametric calibration method used in supervised learning. For performing large-scale IR, we propose a primal-dual active-set (PDAS) algorithm which, in contrast to the state-of-the-art Pool Adjacent Violators (PAV) algorithm, can be parallized and is easily warm-started thus well-suited in the online settings. We prove that, like the PAV algorithm, our PDAS algorithm for IR is convergent and has a work complexity of O(n), though our numerical experiments suggest that our PDAS algorithm is often faster than PAV. In addition, we propose PDAS variants (with safeguarding to ensure convergence) for solving related trend filtering (TF) problems, providing the results of experiments to illustrate their effectiveness. …

Gradient Acceleration in Activation Function (GAAF) google
Dropout has been one of standard approaches to train deep neural networks, and it is known to regularize large models to avoid overfitting. The effect of dropout has been explained by avoiding co-adaptation. In this paper, however, we propose a new explanation of why dropout works and propose a new technique to design better activation functions. First, we show that dropout is an optimization technique to push the input towards the saturation area of nonlinear activation function by accelerating gradient information flowing even in the saturation area in backpropagation. Based on this explanation, we propose a new technique for activation functions, gradient acceleration in activation function (GAAF), that accelerates gradients to flow even in the saturation area. Then, input to the activation function can climb onto the saturation area which makes the network more robust because the model converges on a flat region. Experiment results support our explanation of dropout and confirm that the proposed GAAF technique improves performances with expected properties. …

Analytic Network Learning google
Based on the property that solving the system of linear matrix equations via the column space and the row space projections boils down to an approximation in the least squares error sense, a formulation for learning the weight matrices of the multilayer network can be derived. By exploiting into the vast number of feasible solutions of these interdependent weight matrices, the learning can be performed analytically layer by layer without needing of gradient computation after an initialization. Possible initialization schemes include utilizing the data matrix as initial weights and random initialization. The study is followed by an investigation into the representation capability and the output variance of the learning scheme. An extensive experimentation on synthetic and real-world data sets validates its numerical feasibility. …