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 33713380 of 3569 papers

TitleStatusHype
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained ModelsCode0
Autoregressive Conditional Neural ProcessesCode0
Using Meta-learning to Recommend Process Discovery MethodsCode0
Open-world Learning and Application to Product ClassificationCode0
Meta-Learning Priors for Efficient Online Bayesian RegressionCode0
ADIOS: Antibody Development via Opponent ShapingCode0
Meta-Learning Probabilistic Inference For PredictionCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying ModerationCode0
TaskSet: A Dataset of Optimization TasksCode0
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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