论文标题
羊驼与基于GP的先前学习：两种贝叶斯元学习算法之间的比较
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning Algorithms
论文作者
论文摘要
从计算机视觉到增强学习，已成功地应用了元学习或几次学习的学习。在为元学习提议的许多框架中，当需要准确和校准的不确定性估计值时，贝叶斯方法特别受青睐。在本文中，我们研究了两种最近发表的贝叶斯元学习方法之间的相似性和差异：羊驼（Harrison等人[2018]）和PACOH（Rothfuss等人[2020]）。我们提供理论分析以及跨合成和现实世界数据集的经验基准。尽管使用线性内核使用羊驼在计算时间中具有优势，但通常基于GP的方法提供了更大的灵活性，并且在使用常见内核（例如SE（平方指数）内核时，可以在数据集中获得更好的结果。还讨论了不同损失功能选择的影响。
Meta-learning or few-shot learning, has been successfully applied in a wide range of domains from computer vision to reinforcement learning. Among the many frameworks proposed for meta-learning, bayesian methods are particularly favoured when accurate and calibrated uncertainty estimate is required. In this paper, we investigate the similarities and disparities among two recently published bayesian meta-learning methods: ALPaCA (Harrison et al. [2018]) and PACOH (Rothfuss et al. [2020]). We provide theoretical analysis as well as empirical benchmarks across synthetic and real-world dataset. While ALPaCA holds advantage in computation time by the usage of a linear kernel, general GP-based methods provide much more flexibility and achieves better result across datasets when using a common kernel such as SE (Squared Exponential) kernel. The influence of different loss function choice is also discussed.
