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 831840 of 3569 papers

TitleStatusHype
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Differentiable plasticity: training plastic neural networks with backpropagationCode0
Adaptive Gradient-Based Meta-Learning MethodsCode0
Learning One-Shot Imitation from Humans without HumansCode0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation TypesCode0
DiCE: The Infinitely Differentiable Monte Carlo EstimatorCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
DiCE: The Infinitely Differentiable Monte-Carlo EstimatorCode0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
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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