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The Power of Attention: How the Transformer Model Achieves State-of-the-Art Results
This episode introduces the "Transformer," the new neural network architecture that challenged the traditional encoder-decoder structure used in sequence transduction models. Instead of recurrent or convolutional layers, the Transformer relies on "multi-head self-attention" to process sequential data, enabling it to process information from all positions in the sequence simultaneously. This parallel processing capability leads to faster training times, especially for long sequences. The episode explores the Transformer's impressive performance in machine translation. It also showcases the model's generalization ability, achieving strong results in English constituency parsing.
Article: https://arxiv.org/abs/1706.03762
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By AIThis episode introduces the "Transformer," the new neural network architecture that challenged the traditional encoder-decoder structure used in sequence transduction models. Instead of recurrent or convolutional layers, the Transformer relies on "multi-head self-attention" to process sequential data, enabling it to process information from all positions in the sequence simultaneously. This parallel processing capability leads to faster training times, especially for long sequences. The episode explores the Transformer's impressive performance in machine translation. It also showcases the model's generalization ability, achieving strong results in English constituency parsing.
Article: https://arxiv.org/abs/1706.03762