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 151175 of 3569 papers

TitleStatusHype
Learning to Learn from APIs: Black-Box Data-Free Meta-LearningCode1
Zero- and Few-Shot Event Detection via Prompt-Based Meta LearningCode1
Meta-prediction Model for Distillation-Aware NAS on Unseen DatasetsCode1
Meta-Learning Online Adaptation of Language ModelsCode1
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic SegmentationCode1
Improving Convergence and Generalization Using Parameter SymmetriesCode1
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta LearningCode1
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-LearningCode1
Meta Omnium: A Benchmark for General-Purpose Learning-to-LearnCode1
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series ForecastingCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph CompletionCode1
Meta-optimized Contrastive Learning for Sequential RecommendationCode1
Meta-Learned Models of CognitionCode1
Exploring Effective Factors for Improving Visual In-Context LearningCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Meta-Learning with a Geometry-Adaptive PreconditionerCode1
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
Image Quality-aware Diagnosis via Meta-knowledge Co-embeddingCode1
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEsCode1
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
Learning Distortion Invariant Representation for Image Restoration from A Causality PerspectiveCode1
Structured State Space Models for In-Context Reinforcement LearningCode1
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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