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 461470 of 3569 papers

TitleStatusHype
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer LearningCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
Meta-Learning with Adaptive HyperparametersCode1
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learningCode1
MELD: Meta-Reinforcement Learning from Images via Latent State ModelsCode1
Few-shot Decoding of Brain Activation MapsCode1
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video RecognitionCode1
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmplerCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Bilevel Optimization: Convergence Analysis and Enhanced DesignCode1
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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