Neural net attention mechanisms

On brilliance through selective ignorance

December 20, 2017 — August 5, 2022

language
machine learning
neural nets
NLP
Figure 1

Attention, self attention… What are these things? I am no expert, so see some good blog posts explaining everything:

There is a lot of activity in a particular type of attention network, the transformer, which is a neural network architecture that is very good at processing sequential data, such as text. The transformer is a stack of attention layers, and the attention mechanism is the key to its success.

1 References

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Chen, Chen, Wan, et al. 2021. An Improved Data-Free Surrogate Model for Solving Partial Differential Equations Using Deep Neural Networks.” Scientific Reports.
Choy, Gwak, Savarese, et al. 2016. Universal Correspondence Network.” In Advances in Neural Information Processing Systems 29.
Kim, Mnih, Schwarz, et al. 2019. Attentive Neural Processes.”
Luong, Pham, and Manning. 2015. Effective Approaches to Attention-Based Neural Machine Translation.”
Ortega, Kunesch, Delétang, et al. 2021. Shaking the Foundations: Delusions in Sequence Models for Interaction and Control.” arXiv:2110.10819 [Cs].
Qin, Zhu, Qin, et al. 2019. Recurrent Attentive Neural Process for Sequential Data.”
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Vaswani, Shazeer, Parmar, et al. 2017. Attention Is All You Need.” arXiv:1706.03762 [Cs].
Yang, and Hu. 2020. Feature Learning in Infinite-Width Neural Networks.” arXiv:2011.14522 [Cond-Mat].