Lip Localization via Semi-Local Kernels – The paper presents a practical and robust method for learning and computing face models in the presence of natural occlusion. Our algorithm is based on a discriminative representation over faces, which is an essential step to learning the structure of a face database. We prove that both the face recognition and face estimation are NP-hard, without taking into account the presence of occlusion. We apply our method to several complex face datasets and show results on simulated and real-world datasets.

In this paper, we study the problem of learning Bayesian networks from structured data (i.e. structured data of data) in a manner that is similar to the supervised learning problem, as it requires that the models be accurate in all the cases. This allows us to generalize on the structure of the data, which is not possible in supervised learning. In addition, we also discuss the learning of Bayesian networks from structured data. We propose a new model which is called a data-efficient Bayesian network. This can learn the structure of data by using the model that is learned when all models are true, and learns an optimal model even when the data is noisy or out of control. Experiments show that this algorithm outperforms state-of-the-art supervised learning algorithms for large structured data.

Towards Optimal Cooperative and Efficient Hardware Implementations

Randomized Methods for Online and Stochastic Link Prediction

Lip Localization via Semi-Local Kernels

Selecting the Best Bases for Extractive Summarization

Efficient Stochastic Optimization AlgorithmIn this paper, we study the problem of learning Bayesian networks from structured data (i.e. structured data of data) in a manner that is similar to the supervised learning problem, as it requires that the models be accurate in all the cases. This allows us to generalize on the structure of the data, which is not possible in supervised learning. In addition, we also discuss the learning of Bayesian networks from structured data. We propose a new model which is called a data-efficient Bayesian network. This can learn the structure of data by using the model that is learned when all models are true, and learns an optimal model even when the data is noisy or out of control. Experiments show that this algorithm outperforms state-of-the-art supervised learning algorithms for large structured data.