Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Sequence-to-Sequence Model× | Attention Mechanism× | Multi-Head Self-Attention× | |
|---|---|---|---|
| Vakgebied | Deep learning | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2014 | 2015 | 2017 |
| Grondlegger≠ | Sutskever, I.; Cho, K. | Bahdanau, D.; Luong, M.T. | Vaswani, A. et al. |
| Type≠ | Encoder-decoder neural network (deep learning) | Neural attention layer (encoder-decoder) | Attention mechanism (Transformer core) |
| Oorspronkelijke bron≠ | Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| Aliassen≠ | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention |
| Verwant | 5 | 5 | 5 |
| Samenvatting≠ | The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation. | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. | Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5. |
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