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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 611–620 of 3569 papers

Title	Date	Tasks	Status	Hype
Learning Neural Causal Models from Unknown Interventions	Oct 2, 2019	Meta-Learning	CodeCode Available	1
Overcoming Data Limitation in Medical Visual Question Answering	Sep 26, 2019	DenoisingMedical Visual Question Answering	CodeCode Available	1
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML	Sep 19, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Stacking Models for Nearly Optimal Link Prediction in Complex Networks	Sep 17, 2019	DiversityLink Prediction	CodeCode Available	1
Meta-Learning with Implicit Gradients	Sep 10, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations	Aug 30, 2019	Link PredictionMeta-Learning	CodeCode Available	1
Few-shot Text Classification with Distributional Signatures	Aug 16, 2019	ClassificationFew-Shot Text Classification	CodeCode Available	1
Learning to learn with quantum neural networks via classical neural networks	Jul 11, 2019	Meta-Learning	CodeCode Available	1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes	Jun 18, 2019	Active LearningContinual Learning	CodeCode Available	1
Meta Dropout: Learning to Perturb Features for Generalization	May 30, 2019	BIG-bench Machine LearningMeta-Learning	CodeCode Available	1

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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