Modulating transfer between tasks in gradient-based meta-learning

By Erin Grant, Ghassen Jerfel, Katherine A. Heller, T. Griffiths. 2018

7 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. Sergey Levine
2. Tianhe Yu
3. Chelsea Finn
4. Glen Berseth
5. Pieter Abbeel
6. Abhishek Gupta
7. Han-Jia Ye
8. Yoshua Bengio

Citations in the corpus

• CoMPS: Continual Meta Policy Search (2021, background): “Recent work has made progress toward using supervised meta-learning in an online setting (Ren et al., 2018; Finn et al., 2019; Khodak et al., 2019; Jerfel et al., 2019; Grant et al., 2019; Zhuang et al., 2019; Antoniou et al., 2020; Wallingford et al., 2020; Yu et al., 2020a; Yao et al., 2020).”
• Online Meta-Learning (2019, neutral): “Recent work has considered handling non-stationary task distributions in meta-learning using Dirichlet process mixture models over meta-learned parameters (Grant et al., 2019).”
• Variable-Shot Adaptation for Online Meta-Learning (2020, methodology): “Unlike prior online meta-learning works (Finn et al., 2019; Grant et al., 2019; Khodak et al., 2019; Zhuang et al., 2019; Ren et al., 2020; Antoniou et al., 2020; Wallingford et al., 2020; Yao et al., 2020), we develop an algorithm that is specifically tailored to leverage variable amounts of data, ”