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A novel automatic code completion neural network, which is based on a self-attention mechanism with open vocabulary to address issues of OOV, slow training ...
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To capture the long-term dependency in the input programs, we adopt a self-attentional ar- chitecture based network as the base language model. To enable the.
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A selfattentional neural architecture for code completion with multi-task learning that captures the long-term dependency in the input programs, ...
Sep 12, 2020 · In this paper, we propose a self-attentional neural architecture for code completion with multi-task learning.
A Novel Self-attention Based Automatic Code Completion Neural ...
114.55.40.155 › literature › detail
Code completion is one branch of source code modeling tasks. Using a deep learning method to implement it has explored the possibilities of modeling source ...
Code Completion with Neural Attention and Pointer Networks
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Sep 6, 2024 · Intelligent code completion has become an essential tool to accelerate modern software development. To facilitate effective code completion ...
Jan 18, 2024 · Gaining a deep understanding of how the context information is exchanged and updated within the self-attention mechanism is important for.
Oct 17, 2024 · This method utilizes genomic sequences as input, and based on the training model, PACES provides potential modified sequences (Zhao et al. 2019) ...
We propose a novel source code representation method based on the multi-head attention mechanism (SCRMHA).
In this paper, a novel deep learning model is proposed based on different Convolution Neural Networks (CNN) and the attention mechanism for addressing the HAR ...