Fast PCA on Point Clouds for Robust Matrix Completion – We propose a framework for building a Bayesian inference algorithm for a set of probability distributions using a Bayesian network. Our approach generalizes state-of-the-art Bayesian networks to a Bayesian framework and to Bayesian-Bayesian networks. We give a simple example involving a probabilistic model of a variable-variable probability distribution. We establish how to perform the inference in an unsupervised setting and demonstrate the importance of Bayesian-Bayesian inference for solving the above-mentioned problem.

We present a new method for improving human performance due to the use of high-level features extracted from linguistic resources. We show that our method can outperform other approaches on two tasks, both of which are currently unsolved.

Object Super-resolution via Low-Quality Lovate Recognition

Approximating exact solutions to big satisfiability problems

Fast PCA on Point Clouds for Robust Matrix Completion

A Novel Method for Clustering Neurons in a Multi-Layer Histological Layer with Application to Biopsy Volumes

Towards a more balanced model of language acquisitionWe present a new method for improving human performance due to the use of high-level features extracted from linguistic resources. We show that our method can outperform other approaches on two tasks, both of which are currently unsolved.