Alro10/meta-learning-resources

A curated list of meta-learning resources: papers, coding, tutorials, etc.

Meta-Learning Resources

A curated list of meta-learning papers, code, tutorials, etc.

Under developing!

Figure by BAIR Lab

Table of Contents

• Papers
• Tutorials
• Code

Papers

2019

• Meta-Learning of Neural Architectures for Few-Shot Learning

  • Thomas Elsken, et al.
  • not yet

• Meta-Q-Learning

  • Code-not yet
  • Rasool Fakoor, et al.

• Meta-Learning with Implicit Gradients NeurIPS 2019. First two authors contributed equally

  • Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine.
  • Code- not yet

• Metalearned Neural Memory.

  • Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler.
  • Code- not yet

• Meta-Transfer Learning for Few-Shot Learning. CVPR 2019

  • Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele.
  • [Code-TensorFlow]
  • [Code-Pytorch]

• Meta-learning with differentiable closed-form solvers. ICLR 2019

  • Luca Bertinetto, João F. Henriques, Philip H.S. Torr, Andrea Vedaldi.
  • [Code]

• Meta reinforcement learning as task inference. DeepMind

  • Jan Humplik, Alexandre Galashov, LeonardDeepMind Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess.

2018

• Meta-Reinforcement Learning of Structured Exploration Strategies. NeurIPS 2018

  • Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, Sergey Levine.

2017

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017

  • Chelsea Finn, Pieter Abbeel, Sergey Levine.
  • [Code]

Code

• TensorFlow 2.0 implementation of MAML by Marianne Linhares.