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This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com;
(2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com;
(3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com.
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Table of Links
- Abstract and Introduction
- Problem Setup and Related Work
- HyperTransformer
- Experiments
- Conclusion and References
- A Example of a Self-Attention Mechanism For Supervised Learning
- B Model Parameters
- C Additional Supervised Experiments
- D Dependence On Parameters and Ablation Studies
- E Attention Maps of Learned Transformer Models
- F Visualization of The Generated CNN Weights
- G Additional Tables and Figures
A EXAMPLE OF A SELF-ATTENTION MECHANISM FOR SUPERVISED LEARNING
Self-attention in its rudimentary form can implement a cosine-similarity-based sample weighting, which can also be viewed as a simple 1-step MAML-like learning algorithm. This can be seen by considering a simple classification model
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[5] here we use only local features hφl (ei) of the sample embedding vectors ei
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