Distributed Sparse Signal Recovery


Distributed Sparse Signal Recovery – Nearest-Nest Search involves the search for each user and the performance of these search algorithms, based upon the objective function of the algorithm(s) in each instance of the search objective. In this paper, the goal of this report is to identify the best query solution for each user. The main goal of the work is to find the best algorithm with the optimal search performance. The algorithm based system is based on a data driven approach and some specific rules and parameters were selected for solving search problems. Based on these rules and parameters, the proposed algorithm is implemented and tested.

We present a new approach for learning to paraphrasing, which aims to learn a system that combines natural language processing, reinforcement learning and automatic reasoning with a multi-agent system to effectively mimic the language of human beings. Our approach utilizes a deep learning technique applied at the core of a machine learning framework, which consists of multiple agents. When applied to a natural language processing module, the model learns to paraphrase its natural language and, as a consequence, improve its paraphrasing performance. We also present a novel learning strategy for a multi-agent system, that uses a reinforcement learning strategy to learn to paraphrase its input phrases. Experiments on a large-scale synthetic language translation task show that our approach can translate natural language sentences successfully to human speech recognition tasks, and outperform the standard English Paraphrase and UnParaphrase systems, both of which have been widely used.

Semi-supervised learning for multi-class prediction

Stochastic Nonparametric Learning via Sparse Coding

Distributed Sparse Signal Recovery

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  • A Sparse Gaussian Process Model Based HTM System with Adaptive Noise

    A General Framework for Learning to Paraphrase in Learner WorkbooksWe present a new approach for learning to paraphrasing, which aims to learn a system that combines natural language processing, reinforcement learning and automatic reasoning with a multi-agent system to effectively mimic the language of human beings. Our approach utilizes a deep learning technique applied at the core of a machine learning framework, which consists of multiple agents. When applied to a natural language processing module, the model learns to paraphrase its natural language and, as a consequence, improve its paraphrasing performance. We also present a novel learning strategy for a multi-agent system, that uses a reinforcement learning strategy to learn to paraphrase its input phrases. Experiments on a large-scale synthetic language translation task show that our approach can translate natural language sentences successfully to human speech recognition tasks, and outperform the standard English Paraphrase and UnParaphrase systems, both of which have been widely used.


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