hierarchical attention networks
Hierarchical attention networks are a type of neural network model designed to analyze and understand large amounts of textual data. They employ a hierarchical structure that captures contextual relationships between various levels of information in the text, such as words, sentences, and paragraphs. Attention mechanisms are used to assign varying levels of importance to different parts of the text, allowing for effective understanding and classification of the data.
Requires login.
Related Concepts (1)
Similar Concepts
- attention in graph neural networks
- attention-based models
- attention-based recommendation systems
- attention-based sequence-to-sequence models
- attentional circuits
- attentional networks
- generative adversarial networks with attention
- hierarchical levels
- hierarchical network
- hierarchical structures
- memory attention networks
- multi-head attention
- neural networks
- recurrent neural networks with attention
- reinforcement learning with attention