Bayesian Meta Sampling for Fast Uncertainty Adaptation

By Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen. 2020

13 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zhenyi Wang
2. Li Shen
3. Le Fang
4. Tiehang Duan
5. Mingchen Gao
6. Donglin Zhan
7. Liyuan Wang
8. Liangzu Peng

Citations in the corpus

• Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (2022, neutral): “Various approaches have been proposed [56,54,21,22,24,50,5,40,4,39,34,66,69,65].”
• Meta-learning without data via Wasserstein distributionally-robust model fusion (2022, unknown): “…et al., 2016, Finn et al., 2017, Snell et al., 2017, Ravi and Beatson, 2019, Rajeswaran et al., 2019, Finn et al., 2019b, Raghu et al., 2020, Wang et al., 2020b, Zhou et al., 2021, Bohdal et al., 2021, Rothfuss et al., 2021, CHEN et al., 2021, Bronskill et al., 2021, Sun et al., 2021] focus…”
• Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness (2021, unknown): “Representative works include [56, 51, 19, 20, 22, 48, 7, 41, 6, 40, 36, 60, 45, 52, 64], etc.”
• Learning to Learn and Remember Super Long Multi-Domain Task Sequence (2022, background): “Most existing works of meta-learning [6, 19, 21, 29, 38, 65, 70, 75, 81] focus on stationary task distributions.”