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 391–400 of 3569 papers

TitleStatusHype
Automated Relational Meta-learningCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Learning to Generalize across Domains on Single Test SamplesCode1
Learning to Generalize: Meta-Learning for Domain GeneralizationCode1
Learning To Learn and Remember Super Long Multi-Domain Task SequenceCode1
Learning to Learn and Sample BRDFsCode1
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-LearningCode1
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense DisambiguationCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Curriculum-Meta Learning for Order-Robust Continual Relation ExtractionCode1
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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