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 2871–2880 of 3569 papers

TitleStatusHype
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal DataCode0
Speaker Adaptive Training using Model Agnostic Meta-LearningCode0
Learning to learn by gradient descent by gradient descentCode0
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement LearningCode0
Analyzing the Effectiveness of Quantum Annealing with Meta-LearningCode0
Learning to Generate Noise for Multi-Attack RobustnessCode0
Learning to Forget for Meta-LearningCode0
Consistency of Compositional Generalization across Multiple LevelsCode0
Extreme Algorithm Selection With Dyadic Feature RepresentationCode0
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement LearningCode0
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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