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 376400 of 3569 papers

TitleStatusHype
LEAF: A Benchmark for Federated SettingsCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Learn From Model Beyond Fine-Tuning: A SurveyCode1
Learning Compositional Rules via Neural Program SynthesisCode1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
Auto-Lambda: Disentangling Dynamic Task RelationshipsCode1
Learning Domain Invariant Prompt for Vision-Language ModelsCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Automated Machine Learning Techniques for Data StreamsCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Learning to Detect Noisy Labels Using Model-Based FeaturesCode1
Automated Relational Meta-learningCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Learning to Generalize across Domains on Single Test SamplesCode1
Learning to Generalize: Meta-Learning for Domain GeneralizationCode1
Learning To Learn and Remember Super Long Multi-Domain Task SequenceCode1
Learning to Learn and Sample BRDFsCode1
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-LearningCode1
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense DisambiguationCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Curriculum-Meta Learning for Order-Robust Continual Relation ExtractionCode1
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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