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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 901–910 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation	Apr 5, 2024	AttributeBenchmarking	CodeCode Available	0	5
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing	Apr 10, 2021	Dependency ParsingFew-Shot Learning	CodeCode Available	0	5
A Meta-Learning Framework for Generalized Zero-Shot Learning	Sep 10, 2019	Generalized Zero-Shot LearningMeta-Learning	CodeCode Available	0	5
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks	Jul 6, 2021	BIG-bench Machine LearningDomain Generalization	CodeCode Available	0	5
Meta-Learning for Natural Language Understanding under Continual Learning Framework	Nov 3, 2020	Continual LearningMeta-Learning	CodeCode Available	0	5
Efficient Optimization of Loops and Limits with Randomized Telescoping Sums	May 16, 2019	Meta-LearningVariational Inference	CodeCode Available	0	5
Learning Deep Morphological Networks with Neural Architecture Search	Jun 14, 2021	Edge DetectionMeta-Learning	CodeCode Available	0	5
Efficient time stepping for numerical integration using reinforcement learning	Apr 8, 2021	Meta-LearningNumerical Integration	CodeCode Available	0	5
Learning Fast Adaptation with Meta Strategy Optimization	Sep 28, 2019	Meta-Learning	CodeCode Available	0	5
Attentional Meta-learners for Few-shot Polythetic Classification	Jun 9, 2021	Classificationfeature selection	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