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attention-based LSTM
An attention-based LSTM is a deep learning architecture that integrates a Long Short-Term Memory recurrent neural network with an attention mechanism to process sequential data more effectively. While standard LSTM networks process sequences step by step to capture temporal relationships and long-term dependencies, the addition of an attention mechanism allows the model to assign varying degrees of importance to different hidden states across the sequence. By computing dynamic attention weights based on task-specific context or target inputs, the architecture can selectively focus on the most informative elements rather than compressing the entire sequence into a single static representation. This combination improves accuracy and interpretability in natural language processing and sequence modeling tasks, where certain parts of an input sequence carry greater relevance for a specific objective.
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