A Generalized Sparse Multiclass Approach to Neural Network Embedding – A novel neural network architecture for video manipulation based on a deep neural network (DNN) is proposed. The proposed architecture leverages a deep recurrent neural network (DNN) to model complex object scenes. The DNN is trained by learning feature representations derived from both the underlying CNN as well as on the entire scene. The aim of this research is to explore a more interpretable and effective approach for object manipulation. The proposed architecture can effectively solve well existing object manipulation tasks, while providing a strong performance guarantee with comparable accuracy to existing state-of-the-art methods. As well as exploiting the underlying architecture, it is proposed to model scene dynamics and provide a more accurate prediction as well as a robust representation of object behavior as a whole.

Person recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.

Robust Feature Selection with a Low Complexity Loss

Fast k-means using Differentially Private Low-Rank Approximation for Multi-relational Data

A Generalized Sparse Multiclass Approach to Neural Network Embedding

Adversarial Encoder Encoder

Generating a Robust Multimodal Corpus for Robust Speech RecognitionPerson recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.