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 33313340 of 3569 papers

TitleStatusHype
Adaptive Meta-learner via Gradient Similarity for Few-shot Text ClassificationCode0
Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task ComplexityCode0
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta LearningCode0
BayesPCN: A Continually Learnable Predictive Coding Associative MemoryCode0
Gradient-Based Meta-Learning with Learned Layerwise Metric and SubspaceCode0
Self-Distillation with Meta Learning for Knowledge Graph CompletionCode0
A comparison of small sample methods for Handshape RecognitionCode0
Addressing Catastrophic Forgetting in Few-Shot ProblemsCode0
A Meta-Transfer Objective for Learning to Disentangle Causal MechanismsCode0
Deciphering Trajectory-Aided LLM Reasoning: An Optimization PerspectiveCode0
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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