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 2411–2420 of 3569 papers

TitleStatusHype
Where Do Human Heuristics Come From?—0
Which is the best model for my data?—0
How Does the Task Landscape Affect MAML Performance?—0
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
Wormhole MAML: Meta-Learning in Glued Parameter Space—0
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily—0
Zebra: In-Context and Generative Pretraining for Solving Parametric PDEs—0
Zero-shot meta-learning for small-scale data from human subjects—0
Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer—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