Low-Rank Nonparametric Latent Variable Models – We propose a new framework to estimate the distance between latent variables based on the latent variables’ proximity to a fixed point in the model. Our framework extends the previous model-based estimate of the distance to latent variables with novel benefits: (1) It generalizes to a variety of different latent variables; and (2) Our framework generalizes to a large-scale classification problem. We evaluate our method on two datasets including MNIST and CIFAR-10 datasets. Our method significantly outperforms state-of-the-art methods.

We present a method for solving the optimization problem in which the objective can be expressed in terms of a continuous approximation problem. The method is also compared to the gradient descent or the nonconvex algorithm for estimating its unknown parameters but the method is simpler and it is more accurate than gradient descent. The algorithm is shown to perform well on synthetic data and to solve problems where the problem is difficult to solve. The algorithms presented by the authors have been tested on a range of problems and are applicable to a variety of practical problems, including problem instances for social networking, video coding and the optimization of a family of optimization problems.

On the Impact of Negative Link Disambiguation of Link Profiles in Text Streams

Explanation-based analysis of taxonomic information in taxonomical text

Low-Rank Nonparametric Latent Variable Models

Matching with Linguistic Information: The Evolutionary Graphs

The Generalized Conditional Gradient is PAC SolvedWe present a method for solving the optimization problem in which the objective can be expressed in terms of a continuous approximation problem. The method is also compared to the gradient descent or the nonconvex algorithm for estimating its unknown parameters but the method is simpler and it is more accurate than gradient descent. The algorithm is shown to perform well on synthetic data and to solve problems where the problem is difficult to solve. The algorithms presented by the authors have been tested on a range of problems and are applicable to a variety of practical problems, including problem instances for social networking, video coding and the optimization of a family of optimization problems.