A modular architecture called modular partial connected neural network (MPCNN) has been proposed here. This MPCNN combines three partially connected neural networks (PCNNs) trained on three different feature sets into a hierarchically organized MLP with their truncated subnetwork as basic building ...
An algorithm for evolving neural network via the genetic algorithm based on GPU parallel architecture was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary) and was used on gender face recognition. By using the powerful ability of GP...
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In recent years, deep neural networks achieve significant improvements in automatic speech recognition. In this paper, we propose a deep structure used for robust ASR. The model has several partially connected layers which can suppress noise in different frequency bands. In order to recognize the sp...
It directly feeds 14 × 14 × 500 fea- ture maps to the fully-connected layer for each group lead- ing to a large number of parameters. On the contrary, PS- MCNN uses 3 × 3 kernels throughout the network and in- puts 6 × 5 × 128 feature maps to the fully-connected layers. ...
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Therefore, we select the most connected 200 nodes in these brain regions and binarize them as the input to the WMGM inference algorithm. For reconstruction accuracy, we run the inference algorithm 50 times on each brain network and calculate their mean and standard deviation. For the Medulla ...
To explore both the multi-typed attributes and the semantic relationships flexibly, DeepEmLAN [21] simultaneously preserves the information of topologies, attributes, and labels, respectively, using three closely connected and interacted components. In the DeepEmLAN, the temporary vectors generated by ...
Unless otherwise specified, post-processing techniques (i.e., filling up holes and deleting small connected components) are not applied. We only show Dice and hausdorff distance (HD) for the mean of each dataset. Please refer to Note S6 for training details and complete results under more ...
To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting extit{Deep Recurrent Q-Network} (DRQN), although capable of seeing only a ...