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 2451–2475 of 3569 papers

TitleStatusHype
Automatic Unsupervised Outlier Model Selection—0
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data—0
Auto-Meta: Automated Gradient Based Meta Learner Search—0
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—0
AutoML for Contextual Bandits—0
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration—0
Auto-view contrastive learning for few-shot image recognition—0
Auxiliary learning induced graph convolutional networks—0
Auxiliary task discovery through generate-and-test—0
Backdoor Attacks on Federated Meta-Learning—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Bayesian Active Meta-Learning for Black-Box Optimization—0
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management—0
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Bayesian Meta-Learning for Few-Shot 3D Shape Completion—0
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms—0
Bayesian meta learning for trustworthy uncertainty quantification—0
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors—0
Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation—0
Bayesian Online Meta-Learning—0
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction—0
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes—0
Behaviour-conditioned policies for cooperative reinforcement learning tasks—0
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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