In our model, the cross-attention mechanism learns the correlation between the target speaker and the speech to determine whether the target speaker is present. Based on this correlation, the gate mechanism enables the model to focus on extracting speech when the target is pres...
In addition, a gated fusion network is designed to fuse the cross-document information. The proposed model outperforms the state-of-the-art methods on Chinese National Medical Licensing Examination (CNMLE) dataset, ClinicQA, which contains 27,432 plain text documents and 13,827 CNMLE questions....
• Propose a gated pyramid module to incorporate both low-level and high-level features. • Apply gated path to filter the useful feature and obtain robust semantic context. • Propose the cross-layer attention module to further exploit context from shallow layers. • Refine the noisy upsa...
model=Sequential()model.add(GRU(hidden_dim,input_shape=(sequence_length,input_dim),return_sequences=False))model.add(Dropout(0.2))model.add(Dense(output_dim,activation='softmax'))# 编译模型 model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])# 打印模型结构 mode...
Due to its convolutional layers, the network can learn hierarchical structures in sequences, which are also present in business processes that are subject to non-linear execution patterns. The second approach is a key-value-predict attention network (KVP). Based on an advanced attention mechanism,...
Experiment with different architectures or additional layers like attention mechanisms to improve performance. Stage 6: deployment and monitoring Deployment Deploy the model for real-world prediction tasks. Ensure there's a pipeline for feeding new data into the model and for handling real-time predicti...
attention layers where one operates along height axis and the other along width axis. Each multi-head attention block is made up of the proposed gated axial attention layer. Note that each multi-head attention block has 8 gated axial attention heads. The output from the multi-head attention ...
[34] proposed to connect the encoding and decoding paths with dilated convolutions, where the receptive field of the convolutional layers was improved without changing the feature map sizes. However, the gridding effect might be introduced during the dilated convolutions. In order to solve this ...
proposed gated axial attention layers. In LoGo, we perform local global training for axial attention U-Net without using the gated axial attention layers. In MedT, we use gated axial attention as the basic building block for global branch and axial attention without positional encoding for local...
This network is designed to capture multi-scale features and introduces a cross-branch attention mechanism to emphasize the features of crucial branches, enhancing the model’s performance and robustness. Liu et al. (2023) adopts an organizational structure similar to that of the Inception network,...