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Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 701–710 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to learn by gradient descent by gradient descent	Jun 14, 2016	Meta-Learning	CodeCode Available	0	5
Learning to Learn By Self-Critique	Dec 1, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Learning to Generate Noise for Multi-Attack Robustness	Jun 22, 2020	Meta-Learning	CodeCode Available	0	5
Learning to Learn Cropping Models for Different Aspect Ratio Requirements	Jun 1, 2020	Image CroppingMeta-Learning	CodeCode Available	0	5
Learning to Learn Words from Visual Scenes	Nov 25, 2019	Language AcquisitionLanguage Modelling	CodeCode Available	0	5
Learning to Forget for Meta-Learning	Jun 13, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers	Jun 4, 2019	Decision MakingMeta-Learning	CodeCode Available	0	5
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks	Nov 10, 2019	DiversityEntity Typing	CodeCode Available	0	5
Analyzing the Effectiveness of Quantum Annealing with Meta-Learning	Aug 1, 2024	Meta-Learning	CodeCode Available	0	5
Learning to Explore for Stochastic Gradient MCMC	Aug 17, 2024	image-classificationImage Classification	CodeCode Available	0	5

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All datasets ML10 MT50 ML45 OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MZ+Recon	Meta-train success rate	97.8	—	Unverified
2	MZ	Meta-train success rate	97.6	—	Unverified
3	MAML	Meta-test success rate	36	—	Unverified
4	RL^2	Meta-test success rate	10	—	Unverified
5	DnC	Meta-test success rate	5.4	—	Unverified
6	PEARL	Meta-test success rate	0	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SoftModule	Average Success Rate	60	—	Unverified
2	Multi-task multi-head SAC	Average Success Rate	35.85	—	Unverified
3	DisCor	Average Success Rate	26	—	Unverified
4	NDP	Average Success Rate	11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MZ+Recon	Meta-test success rate (zero-shot)	18.5	—	Unverified
2	MZ	Meta-test success rate (zero-shot)	17.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Metadrop	% Test Accuracy	95.75	—	Unverified