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 1376–1400 of 3569 papers

TitleStatusHype
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition—0
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed RecognitionCode1
Scalable Adversarial Online Continual LearningCode0
Generalization in Neural Networks: A Broad Survey—0
Learning Differential Operators for Interpretable Time Series Modeling—0
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task DistributionsCode0
The Neural Process Family: Survey, Applications and PerspectivesCode1
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction—0
Anti-Retroactive Interference for Lifelong LearningCode0
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Wasserstein Task Embedding for Measuring Task SimilaritiesCode0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
Q-Net: Query-Informed Few-Shot Medical Image SegmentationCode1
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
Quantum Multi-Agent Meta Reinforcement Learning—0
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails—0
Meta Learning for High-dimensional Ising Model Selection Using _1-regularized Logistic Regression—0
IPNET:Influential Prototypical Networks for Few Shot Learning—0
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition—0
Meta-Learning Online Control for Linear Dynamical Systems—0
Meta Sparse Principal Component Analysis—0
CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks—0
Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning—0
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning—0
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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