Meta-learning

tags

Machine learning

Constrained meta-learning

(Kirsch, Schmidhuber 2021)

Meta-learning of initialization

The goal is to learn the initialization of neural network parameters or recurrent neural network initial states in order to make the training faster or less prone to getting stuck in local minima.

Example for implicit neural representations: (Tancik et al. 2021)

Meta-learning algorithms

MAML

(Finn et al. 2017)

Reptile

(Nichol et al. 2018)

Bibliography

1. Louis Kirsch, Jürgen Schmidhuber. February 16, 2021. http://arxiv.org/abs/2012.14905.
2. Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng. March 23, 2021. http://arxiv.org/abs/2012.02189. See notes
3. Chelsea Finn, Pieter Abbeel, Sergey Levine. 2017. In , 70:10.
4. Alex Nichol, Joshua Achiam, John Schulman. October 22, 2018. http://arxiv.org/abs/1803.02999.