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Bookmark or Share- Attention Is All You Need - arXiv.orgAn attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum
- [1706.03762] Attention Is All You Need - arXiv.orgAttention Is All You Need. The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism.
- Attention Is All You NeedAn attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum
- [2010.13154] Attention is All You Need in Speech Separation - arXiv.orgTransformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism. In this paper, we propose the SepFormer, a novel RNN-free Transformer-based neural network for speech separation.
- [1706.03762v5] Attention Is All You Need - arXivAttention Is All You Need. (Submitted on 12 Jun 2017 (v1), revised 6 Dec 2017 (this version, v5), latest version 2 Aug 2023 (v7)) The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
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