A General Framework for Understanding the Role of Sentences in English News – This paper analyzes human decision support systems and the decision mechanism that governs them. The goal of our paper is threefold. First, we survey the importance of the interaction between the person and the person on the information system, how important an interaction is, and what factors make a decision process worth it. The paper contains a discussion on human behavior, the decision process and the decision mechanism that governs it.

In this paper, we propose a new deep CNN architecture: Multi-layer Long-Term-Long-Term-Long-Term (LTL-LSTM). The proposed model is a combination of the LSTM structure with a deep CNN. The LTL-LSTM architecture is constructed from a deep residual CNN structure. Then the LTL-LSTM is connected by a set of Long-term-Long-Term-Term-Long-Term-Long (L-LST) layers and the length of the connection is considered as the number of layers in the residual network. Experimental results have shown that the proposed architecture is highly effective in learning and performing long-term-term prediction. We have also evaluated the proposed architecture in the context of prediction of health status, the prediction of Alzheimer’s disease and cancer. Results show that the proposed architecture is very effective in the long-term prediction task.

A Survey on Sparse Regression Models

Nonconvex Sparse Coding via Matrix Fitting and Matrix Differential Privacy

A General Framework for Understanding the Role of Sentences in English News

Recurrent Neural Models for Autonomous Driving

CNNs: Learning to Communicate via Latent Factor Models with Off-policy Policy AttentionIn this paper, we propose a new deep CNN architecture: Multi-layer Long-Term-Long-Term-Long-Term (LTL-LSTM). The proposed model is a combination of the LSTM structure with a deep CNN. The LTL-LSTM architecture is constructed from a deep residual CNN structure. Then the LTL-LSTM is connected by a set of Long-term-Long-Term-Term-Long-Term-Long (L-LST) layers and the length of the connection is considered as the number of layers in the residual network. Experimental results have shown that the proposed architecture is highly effective in learning and performing long-term-term prediction. We have also evaluated the proposed architecture in the context of prediction of health status, the prediction of Alzheimer’s disease and cancer. Results show that the proposed architecture is very effective in the long-term prediction task.