เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| กลไกการใส่ใจ (Attention Mechanism)× | หน่วยความจำแบบวนซ้ำแบบมีประตู (Gated Recurrent Unit - GRU)× | แอลเอสทีเอ็ม× | |
|---|---|---|---|
| สาขาวิชา | การเรียนรู้เชิงลึก | การเรียนรู้เชิงลึก | การเรียนรู้เชิงลึก |
| ตระกูล | Machine learning | Machine learning | Machine learning |
| ปีกำเนิด≠ | 2015 | 2014 | 1997 |
| ผู้ริเริ่ม≠ | Bahdanau, D.; Luong, M.T. | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. | Hochreiter, S. & Schmidhuber, J. |
| ประเภท≠ | Neural attention layer (encoder-decoder) | Recurrent neural network with gating | Recurrent neural network (gated memory cell) |
| แหล่งต้นตำรับ≠ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| ชื่อเรียกอื่น≠ | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | GRU, GRU network, gated RNN, GRU cell | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells |
| ที่เกี่ยวข้อง≠ | 5 | 3 | 5 |
| สรุป≠ | 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. | The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM. | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. |
| ScholarGateชุดข้อมูล ↗ |
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