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 32513260 of 3569 papers

TitleStatusHype
Sequential Scenario-Specific Meta Learner for Online RecommendationCode0
DiCE: The Infinitely Differentiable Monte Carlo EstimatorCode0
An Investigation of Few-Shot Learning in Spoken Term ClassificationCode0
Truncated Back-propagation for Bilevel OptimizationCode0
One-shot skill assessment in high-stakes domains with limited data via meta learningCode0
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-LearningCode0
TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypesCode0
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot LearningCode0
HIDRA: Head Initialization across Dynamic targets for Robust ArchitecturesCode0
One-Shot Neural Architecture Search via Compressive SensingCode0
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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