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.

With the advent of deep learning (DL), the training problem for deep neural networks (DNNs) has become very challenging. It involves extracting features from the input data in order to achieve a desired solution. However, it is often the only possible solution which can be efficiently achieved. To tackle this problem, the training process can be very parallelized. In this work we propose a novel multi-task learning framework for deep RL based on Multi-task Convolutional Neural Networks (Mt. Conv.RNN), which is capable of training multiple deep RL models simultaneously on multiple DNNs. The proposed method uses a hybrid deep RL framework to tackle the parallelization problem in a single application. The proposed method also provides fast and easy to use pipelines based on batch coding. Extensive experiments shows that the proposed multi-task deep RL method is able to achieve state-of-the-art accuracy on real-world datasets, even with training time of several hours on a small subset of datasets such as HumanKernel, and outperforms the state-of-the-art DNN based method on multiple datasets.

Recurrent Topic Models for Sequential Segmentation

Semantic Parsing with Long Short-Term Memory

Distributed Sparse Signal Recovery

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  • Heteroscedasticity-Aware Image Segmentation by Unsupervised Feature and Kernel Learning with Asymmetric Rank Aggregation

    Multi-target HOG Segmentation: Estimation of localized hGH values with a single high dimensional image bit-streamWith the advent of deep learning (DL), the training problem for deep neural networks (DNNs) has become very challenging. It involves extracting features from the input data in order to achieve a desired solution. However, it is often the only possible solution which can be efficiently achieved. To tackle this problem, the training process can be very parallelized. In this work we propose a novel multi-task learning framework for deep RL based on Multi-task Convolutional Neural Networks (Mt. Conv.RNN), which is capable of training multiple deep RL models simultaneously on multiple DNNs. The proposed method uses a hybrid deep RL framework to tackle the parallelization problem in a single application. The proposed method also provides fast and easy to use pipelines based on batch coding. Extensive experiments shows that the proposed multi-task deep RL method is able to achieve state-of-the-art accuracy on real-world datasets, even with training time of several hours on a small subset of datasets such as HumanKernel, and outperforms the state-of-the-art DNN based method on multiple datasets.


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