SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3210 of 3569 papers

TitleStatusHype
Learning To Learn by Jointly Optimizing Neural Architecture and Weights—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks—0
Learning to Learn for Few-shot Continual Active Learning—0
Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
Learning to Learn Morphological Inflection for Resource-Poor Languages—0
Learning to Learn Neural Networks—0
Learning-to-learn non-convex piecewise-Lipschitz functions—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified