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 3401–3425 of 3569 papers

TitleStatusHype
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningCode0
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial AttacksCode0
PABBO: Preferential Amortized Black-Box OptimizationCode0
Generalizing Reward Modeling for Out-of-Distribution Preference LearningCode0
PAC-Bayes Bounds for Meta-learning with Data-Dependent PriorCode0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Understanding Prompt Tuning and In-Context Learning via Meta-LearningCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform StabilityCode0
Data-Driven Performance Guarantees for Classical and Learned OptimizersCode0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
Data-driven Meta-set Based Fine-Grained Visual ClassificationCode0
Image Deformation Meta-Networks for One-Shot LearningCode0
AutoML in Heavily Constrained ApplicationsCode0
Meta-Learning Parameterized SkillsCode0
Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-LearningCode0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
Generalizable Representation Learning for fMRI-based Neurological Disorder IdentificationCode0
Meta-Learning Update Rules for Unsupervised Representation LearningCode0
Meta-learning using privileged information for dynamicsCode0
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin EffectCode0
Sequential Skip Prediction with Few-shot in Streamed Music ContentsCode0
Frosting Weights for Better Continual TrainingCode0
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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