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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 841–850 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Continually Learn Rapidly from Few and Noisy Data	Mar 6, 2021	Continual LearningMeta-Learning	CodeCode Available	0	5
Learning to Design RNA	Dec 31, 2018	CPUDeep Reinforcement Learning	CodeCode Available	0	5
Differentiable plasticity: training plastic neural networks with backpropagation	Apr 6, 2018	Lifelong learningMeta-Learning	CodeCode Available	0	5
Adaptive Gradient-Based Meta-Learning Methods	Jun 6, 2019	Federated LearningFew-Shot Learning	CodeCode Available	0	5
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks	May 30, 2019	Bayesian InferenceMeta-Learning	CodeCode Available	0	5
Learning Task-Aware Energy Disaggregation: a Federated Approach	Apr 14, 2022	Federated LearningMeta-Learning	CodeCode Available	0	5
DiCE: The Infinitely Differentiable Monte Carlo Estimator	Jul 1, 2018	Meta-LearningReinforcement Learning	CodeCode Available	0	5
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning	Dec 16, 2021	Meta-Learning	CodeCode Available	0	5
DiCE: The Infinitely Differentiable Monte-Carlo Estimator	Feb 14, 2018	Meta-LearningReinforcement Learning	CodeCode Available	0	5
Adaptive Mixing of Auxiliary Losses in Supervised Learning	Feb 7, 2022	DenoisingKnowledge Distillation	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