Summary of Accelerating Convergence in Bayesian Few-shot Classification, by Tianjun Ke et al.
Accelerating Convergence in Bayesian Few-Shot Classification
by Tianjun Ke, Haoqun Cao, Feng Zhou
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper integrates mirror descent-based variational inference into Gaussian process-based few-shot classification, addressing non-conjugate inference challenges. By leveraging non-Euclidean geometry, mirror descent provides accelerated convergence and exhibits parameterization invariance. Experimental results demonstrate competitive accuracy, improved uncertainty quantification, and faster convergence compared to baseline models. The paper also investigates hyperparameter and component impacts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn quickly from just a few examples. It combines two techniques: Gaussian processes and Bayesian inference. This combination allows the model to learn efficiently and make accurate predictions. The results show that this approach works well, is fast, and can provide uncertainty estimates. Additionally, the authors explored how different settings affect the performance. |
Keywords
» Artificial intelligence » Bayesian inference » Classification » Few shot » Hyperparameter » Inference
