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 941950 of 3569 papers

TitleStatusHype
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
Decoder Choice Network for Meta-LearningCode0
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based TrainingCode0
Episodic Multi-Task Learning with Heterogeneous Neural ProcessesCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Deciphering Trajectory-Aided LLM Reasoning: An Optimization PerspectiveCode0
Asynchronous Distributed Bilevel OptimizationCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning to Continually Learn Rapidly from Few and Noisy DataCode0
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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