Scalable Wavefront Parallel Streaming Deblocking Filter Hardware for HEVC Decoder

Baldev, Swamy; Anumandla, Kiran Kumar; Peesapati, Rangababu

doi:10.1109/tce.2019.2960565

Cited by 7 publications

(1 citation statement)

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“…where v, W z , and W h represent the model parameters to be learned. By improving the monitoring procedures in the LSTM network, the model can measure the weight difference at the end of the language, which solves the problem of distance from the LSTM model and improves the standard performance [15,16].…”

Section: English Machine Translation Model Based On Lstm Attention Em...mentioning

confidence: 99%

Design of English Translation Model Based on Recurrent Neural Network

Wang

2022

Mathematical Problems in Engineering

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In order to improve the accuracy and stability of English translation, this paper proposes an English translation model based on recurrent neural network. Based on the end-to-end encoder-decoder architecture, a recursive neural network (RNN) English machine translation model is designed to promote machine autonomous learning features, transform the distributed corpus data into word vectors, and directly map the source language and target language through the recurrent neural network. Selecting semantic errors to construct the objective function during training can well balance the influence of each part of the semantics and fully consider the alignment information, providing a strong guidance for the training of deep recurrent neural networks. The experimental results show that the English translation model based on recurrent neural network has high effectiveness and stability. Compared with the baseline system, it has improved about 1.51–1.86 BLEU scores. Conclusion. The model improves the performance and quality of English machine translation model, and the translation effect is better.

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Section: English Machine Translation Model Based On Lstm Attention Em...mentioning

confidence: 99%