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 9761000 of 3569 papers

TitleStatusHype
Learning Fast Adaptation with Meta Strategy OptimizationCode0
Data Valuation using Reinforcement LearningCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal PredictionCode0
Learning Low-Dimensional Embeddings for Black-Box OptimizationCode0
Bootstrapped Meta-LearningCode0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
Dataset Distillation with Infinitely Wide Convolutional NetworksCode0
Modeling Human Exploration Through Resource-Rational Reinforcement LearningCode0
Dataset Distillation using Neural Feature RegressionCode0
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware PromptingCode0
Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-LearningCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial TransferabilityCode0
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
Adaptive Cross-Modal Few-Shot LearningCode0
AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation BatteriesCode0
A Stylometric Inquiry into Hyperpartisan and Fake NewsCode0
Learning advisor networks for noisy image classificationCode0
Exploring the similarity of medical imaging classification problemsCode0
Exploring the Similarity of Representations in Model-Agnostic Meta-LearningCode0
Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-ModulationCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Data-Driven Performance Guarantees for Classical and Learned OptimizersCode0
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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