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 891900 of 3569 papers

TitleStatusHype
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Forecasting Early with Meta LearningCode0
Towards a population-informed approach to the definition of data-driven models for structural dynamics0
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured LearningCode1
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model0
Towards Task Sampler Learning for Meta-LearningCode1
Exploiting Field Dependencies for Learning on Categorical DataCode0
A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access0
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
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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