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 776800 of 3569 papers

TitleStatusHype
Automatic selection of clustering algorithms using supervised graph embeddingCode0
Learning to Learn By Self-CritiqueCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Learning to Forget for Meta-LearningCode0
Learning to Learn Cropping Models for Different Aspect Ratio RequirementsCode0
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement LearningCode0
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine LearningCode0
Memory Efficient Neural Processes via Constant Memory Attention BlockCode0
Learning to Explore for Stochastic Gradient MCMCCode0
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain ValidationCode0
Learning to Design RNACode0
Learning to Defer to a Population: A Meta-Learning ApproachCode0
Learning to Demodulate from Few Pilots via Offline and Online Meta-LearningCode0
Learning to learn ecosystems from limited data -- a meta-learning approachCode0
Learning to Evolve on Dynamic GraphsCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
Discovering Weight Initializers with Meta LearningCode0
Learning to Continually Learn Rapidly from Few and Noisy DataCode0
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution TasksCode0
3FM: Multi-modal Meta-learning for Federated TasksCode0
Learning to Generate Noise for Multi-Attack RobustnessCode0
Automatic Short Math Answer Grading via In-context Meta-learningCode0
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningCode0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
AutoLoss: Learning Discrete Schedules for Alternate OptimizationCode0
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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