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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1801–1825 of 3569 papers

TitleStatusHype
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Recurrent Inference Machine for Medical Image Registration—0
Recurring Concept Meta-learning for Evolving Data Streams—0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks—0
Referee-Meta-Learning for Fast Adaptation of Locational Fairness—0
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning—0
Regularizing Neural Networks with Meta-Learning Generative Models—0
REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents—0
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization—0
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
Reinforcement Teaching—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records—0
RELF: Robust Regression Extended with Ensemble Loss Function—0
REMEDI: Relative Feature Enhanced Meta-Learning with Distillation for Imbalanced Prediction—0
REMIX: Automated Exploration for Interactive Outlier Detection—0
Representation based and Attention augmented Meta learning—0
Representation Change in Model-Agnostic Meta-Learning—0
Reservoir Computing for Fast, Simplified Reinforcement Learning on Memory Tasks—0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning—0
Rethinking Class Imbalance in Machine Learning—0
Show:102550
← PrevPage 73 of 143Next →
All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified