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In Section 3, we propose several modifications to the convolutional model, improving its performance even further.
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Every new iteration has brought small modifications to the space.
Work also started on modifications to The Other Place.
The modifications to the bank agreement had been expected.
Code-Level Modifications to the G2.
The modified BCJR algorithm is based on the 1D JSCC decoder modification shown in [11] for convolutional codes; however, we have made additional minor modification to the algorithm in order to make the algorithm better suited with block codes.
"It's a modification to the modification".
We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture.
Actually, it is interesting to investigate the proper way to combine the convolutional layers.
In order to overcome these defects, we firstly used the immune mechanism to improve the convolutional neural network and put forward a novel immune convolutional neural network algorithm, after we analyzed the network structure and parameters of the convolutional neural network.
The guiding network is adaptively appended to the pedestrian region of the last convolutional layer; the guiding network helps the DCNN to learn the convolutional layers for pedestrian features by focusing on the pedestrian region.
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