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 1941–1950 of 3569 papers

TitleStatusHype
Open Domain Generalization with Domain-Augmented Meta-Learning—0
Optimal allocation of data across training tasks in meta-learning—0
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization—0
Acceleration in Policy Optimization—0
Optimistic Meta-Gradients—0
Optimization of Lightweight Malware Detection Models For AIoT Devices—0
Optimized Generic Feature Learning for Few-shot Classification across Domains—0
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach—0
Optimizing quantum heuristics with meta-learning—0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems—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