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 801825 of 3569 papers

TitleStatusHype
Learning to Explore for Stochastic Gradient MCMCCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
AALF: Almost Always Linear ForecastingCode0
Diverse Preference Augmentation with Multiple Domains for Cold-start RecommendationsCode0
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement LearningCode0
3FM: Multi-modal Meta-learning for Federated TasksCode0
Learning to Evolve on Dynamic GraphsCode0
Learning to Defer to a Population: A Meta-Learning ApproachCode0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Learning to Demodulate from Few Pilots via Offline and Online Meta-LearningCode0
AutoLoss: Learning Discrete Schedules for Alternate OptimizationCode0
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label LearningCode0
Learning to Continually Learn Rapidly from Few and Noisy DataCode0
Learning to Design RNACode0
Domain Adaptive Dialog Generation via Meta LearningCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior KnowledgeCode0
MetaAug: Meta-Data Augmentation for Post-Training QuantizationCode0
Meta-Learning for Natural Language Understanding under Continual Learning FrameworkCode0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningCode0
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning AlgorithmsCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-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