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

Showing 32913300 of 3569 papers

TitleStatusHype
Meta-Learning for Resampling Recommendation SystemsCode0
Scalable Online Recurrent Learning Using Columnar Neural NetworksCode0
Scalable Semi-Modular Inference with Variational Meta-PosteriorsCode0
Hacking Task Confounder in Meta-LearningCode0
Guiding Policies with Language via Meta-LearningCode0
Meta-Learning for Simple Regret MinimizationCode0
Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical SystemsCode0
Two-stage Optimization for Machine Learning WorkflowCode0
Meta-Learning for Stochastic Gradient MCMCCode0
Meta-Learning for Symbolic Hyperparameter DefaultsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified