Summary of Self-attention Through Kernel-eigen Pair Sparse Variational Gaussian Processes, by Yingyi Chen et al.
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
by Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A.K. Suykens
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed KEP-SVGP method leverages the strengths of Transformers while mitigating their limitations by introducing calibrated uncertainty estimation. Building upon Gaussian processes (GPs) with asymmetric attention kernels, KEP-SVGP tackles this asymmetry using kernel SVD (KSVD). This allows for reduced complexity in deriving posteriors and optimizing variational parameters and network weights. The method is evaluated on various benchmarks, showcasing excellent performances and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to make predictions more accurate while also being able to say how certain we are about those predictions. It does this by combining two powerful ideas: Transformers, which are very good at understanding language, and Gaussian processes, which can help us understand uncertainty. The new method, called KEP-SVGP, is able to capture the asymmetry of attention kernels, making it a more robust approach. This could have important implications for many areas where accurate predictions are critical. |
Keywords
* Artificial intelligence * Attention
