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 1201–1250 of 3569 papers

TitleStatusHype
Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge GraphsCode0
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity RecognitionCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Latent Task-Specific Graph Network SimulatorsCode0
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior KnowledgeCode0
learn2learn: A Library for Meta-Learning ResearchCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Learning to Learn without Forgetting using AttentionCode0
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing PlatformsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Joint inference and input optimization in equilibrium networksCode0
An Investigation of the Bias-Variance Tradeoff in Meta-GradientsCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
MACFE: A Meta-learning and Causality Based Feature Engineering FrameworkCode0
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?Code0
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-LearningCode0
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-LearningCode0
CMML: Contextual Modulation Meta Learning for Cold-Start RecommendationCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation ProcessingCode0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Few-shot Conformal Prediction with Auxiliary TasksCode0
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
Clustering Indices based Automatic Classification Model SelectionCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Interpretable Meta-Measure for Model PerformanceCode0
Continuous Meta-Learning without TasksCode0
Learning to Evolve on Dynamic GraphsCode0
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative EmbeddingsCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Few-shot classification in Named Entity Recognition TaskCode0
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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