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 2761–2770 of 3569 papers

TitleStatusHype
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data—0
Meta-Learning over Time for Destination Prediction Tasks—0
Meta-Learning PAC-Bayes Priors in Model Averaging—0
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization—0
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks—0
Meta-learning PINN loss functions—0
Meta-Learning Priors for Safe Bayesian Optimization—0
Meta-learning Pseudo-differential Operators with Deep Neural Networks—0
Meta-Learning Regrasping Strategies for Physical-Agnostic Objects—0
Meta-Learning Reliable Priors in the Function Space—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