Dec 31, 2017 · At pooling stage, attention pooling is adopted to capture most significant information with the guide of Tree-LSTM (a variant of Recurrent NN) ...
A novel structure to combine the strength of CNN, recurrent neural network and recursive neural networks for semantic modelling of sentences outperforms ...
At pooling stage, attention pooling is adopted to capture most significant information with the guide of Tree-LSTM (a variant of Recurrent NN) sentence ...
A novel structure to combine the strength of CNN, recurrent neural network and recursive neural networks for semantic modelling of sentences
Jun 15, 2023 · In this study, we introduce an attention mechanism into Child-Sum Tree-LSTMs for the detection of biomedical event triggers.
We describe an attentive encoder that combines tree-structured recursive neural networks and se- quential recurrent neural networks for modelling sentence ...
Missing: DCNN | Show results with:DCNN
Oct 22, 2024 · Convolutional neural network has been proven to be a powerful semantic composition model for modelling sentences. A standard convolutional ...
In this work, we propose a semantic flow-guided two-stage framework for shape-aware face swapping, namely FlowFace. Unlike most previous methods that focus on ...
Specifically, a tree-structured LSTM is used to encode the syntactic structure of the question sentence. A spatial-semantic attention model is proposed to learn ...
Missing: DCNN | Show results with:DCNN
Our multi-cascaded model employs three supervised feature learners (cascades) based on CNN and LSTM networks with and without soft-attention. The learned ...