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In this paper we develop a whole set of parallel algorithms for improving the computation efficiency of a neurodynamic optimization (NDO) system proposed in our previous work recently. The NDO method is able to solve the sparse signal recovery problems in compressive sensing with the globally convergent optimal solution approximating to the L0 norm minimization, but has the shortcoming with heavy...
Multi-Column Deep Neural Networks achieve state of the art recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human accuracy. This performance is the result of averaging 11-layers deep networks with hundreds of maps per layer, trained on raw, distorted images to prevent them from overfitting. The entire framework runs on a normal desktop...
Runtime monitors check the execution of a system under scrutiny against a set of formal specifications describing a prescribed behaviour. The two core properties for monitoring systems are scalability and adaptability. In this paper we show how RuleRunner, our previous neural-symbolic monitoring system, can exploit learning strategies in order to integrate desired deviations with the initial set of...
Although deep neural networks (DNNs) have achieved great performance gain, the immense computational cost of DNN model training has become a major block to utilize massive speech data for DNN training. Previous research on DNN training acceleration mostly focussed on hardware-based parallelization. In this paper, node pruning and arc restructuring are proposed to explore model redundancy after a novel...
A deep belief network (DBN) is an important branch of deep learning models and has been successfully applied in many machine learning and pattern recognition fields such as computer vision and speech recognition. However, the training of billions of parameters in DBN is computationally challenging for modern central processing units (CPUs). Many studies have reported the efficient implementations...
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