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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 761–770 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning	May 27, 2019	Decision MakingDeep Reinforcement Learning	CodeCode Available	0	5
AutoML: Exploration v.s. Exploitation	Dec 23, 2019	AutoMLMeta-Learning	CodeCode Available	0	5
Learning to Defer to a Population: A Meta-Learning Approach	Mar 5, 2024	Meta-LearningTraffic Sign Detection	CodeCode Available	0	5
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning	Aug 23, 2019	Meta-Learning	CodeCode Available	0	5
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification	Sep 10, 2022	Few-Shot Text ClassificationMeta-Learning	CodeCode Available	0	5
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks	Oct 31, 2019	Dialogue GenerationDiversity	CodeCode Available	0	5
Learning to Design RNA	Dec 31, 2018	CPUDeep Reinforcement Learning	CodeCode Available	0	5
AALF: Almost Always Linear Forecasting	Sep 16, 2024	Decision MakingDeep Learning	CodeCode Available	0	5
Learning to Continually Learn Rapidly from Few and Noisy Data	Mar 6, 2021	Continual LearningMeta-Learning	CodeCode Available	0	5
Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI	Sep 10, 2024	Meta-LearningTransfer Learning	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