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

Showing 161170 of 3569 papers

TitleStatusHype
Meta Omnium: A Benchmark for General-Purpose Learning-to-LearnCode1
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series ForecastingCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph CompletionCode1
Meta-optimized Contrastive Learning for Sequential RecommendationCode1
Meta-Learned Models of CognitionCode1
Exploring Effective Factors for Improving Visual In-Context LearningCode1
Meta-Learning with a Geometry-Adaptive PreconditionerCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified