In this paper, we propose a gated cross-attention network for universal speaker extraction. 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,...
In this paper, we propose a Co-Attention-based Multi-document Inference (CAMI) framework for better reasoning over multiple documents. The proposed framework makes use of not only the attentional information among questions, answers and support documents but also the complementary attentional information...
• 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...
[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 ...
attention network, we use axial attention U-Net with all its axial attention layers replaced with the 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...
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 multihead attention block has 8 gated axialattention heads. The output from the multi-head attention ...
MFCANN: A feature diversification framework based on local and global attention for human activity recognition 2024, Engineering Applications of Artificial Intelligence Citation Excerpt : This network is designed to capture multi-scale features and introduces a cross-branch attention mechanism to emphasize ...