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 2801–2825 of 3569 papers

TitleStatusHype
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesCode1
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction—0
Taming the Herd: Multi-Modal Meta-Learning with a Population of Agents—0
Meta-Learning for Recalibration of EMG-Based Upper Limb Prostheses—0
Attentive Feature Reuse for Multi Task Meta learning—0
BI-MAML: Balanced Incremental Approach for Meta Learning—0
Learning-to-Learn Personalised Human Activity Recognition Models—0
Backdoor Attacks on Federated Meta-Learning—0
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression—0
Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient OptimizationCode1
Learning to Learn Kernels with Variational Random FeaturesCode0
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link PredictionCode1
MC-BERT: Efficient Language Pre-Training via a Meta ControllerCode1
Learning to Stop While Learning to PredictCode1
Meta-Learning Bandit Policies by Gradient Ascent—0
Multi-step Estimation for Gradient-based Meta-learning—0
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
Brain-inspired global-local learning incorporated with neuromorphic computing—0
Meta-Model-Based Meta-Policy Optimization—0
Meta Dialogue Policy Learning—0
Meta Learning as Bayes Risk Minimization—0
Interpretable Meta-Measure for Model PerformanceCode0
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Training Noise-Robust Deep Neural Networks via Meta-LearningCode1
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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