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 2031–2040 of 3569 papers

TitleStatusHype
The Role of Global Labels in Few-Shot Classification and How to Infer Them—0
The Sample Complexity of Meta Sparse Regression—0
The Self-Learning Agent with a Progressive Neural Network Integrated Transformer—0
Thompson Sampling with Diffusion Generative Prior—0
Time Associated Meta Learning for Clinical Prediction—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
TinyReptile: TinyML with Federated Meta-Learning—0
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions—0
TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning—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