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

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Showing 721–730 of 3569 papers

TitleStatusHype
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift—0
Bilevel Programming for Hyperparameter Optimization and Meta-Learning—0
A Comprehensive Survey of Dataset Distillation—0
Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis—0
Bi-Level Meta-Learning for Few-Shot Domain Generalization—0
A Meta-Reinforcement Learning Approach to Process Control—0
Bilevel Continual Learning—0
Bias-Tolerant Fair Classification—0
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition—0
Efficient Model Selection for Time Series Forecasting via LLMs—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