Hospedales et al 2020 - Meta-Learning in Neural Networks: A Survey

2019 - AutoML: A Survey of the State-of-the-Art

2019 - Benchmark and Survey of Automated Machine Learning Frameworks

2020 - Automated Machine Learning: The New Wave of Machine Learning

Hochreiter & Younger 2001 - Learning to Learn Using Gradient Descent Amazing paper from one of the inventors of LSTMs. Jeurgen talks about it here.

Andrychowicz et al 2016 - Learning to Learn by Gradient Descent by Gradient Descent

Ha et al 2016 - HyperNetworks

Ravi & Larochelle 2016 - Optimization as a Model for Few-Shot Learning Proposes “an LSTM-based meta-learner model to learn the exact optimization algorithm used to train another learner neural network classifier in the few-shot regime.”

Chen 2017 - Learning to Learn without Gradient Descent by Gradient Descent Learns a black-box optimizer (gradient-free optimizer). Can be applied to hyperparameter tuning.

MAML: Finn et al 2017 - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Finn et al 2019 - Online Meta-Learning Introduces online meta-learning, where the learner sees a series of tasks. Proposes Follow The Meta-Leader (FTML), a follow-the-leader-type meta-learning algorithm, which extend MAML to this setting.

Kirsch & Schmidhuber 2021 - Meta Learning Backpropagation And Improving It

Zhmoginov et al 2022 - HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning (interview)

Real et al 2020 - AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Peng et al 2021 - PyGlove: Symbolic Programming for Automated Machine Learning

Bansal et al 2019 - Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks

Murty et al 2021 - DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference

Bansal et al 2021 - Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

Application: Optimization

Neural Architecture Search

Multi-Task Learning

Prompting and In-Context Learning