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 1826–1850 of 3569 papers

TitleStatusHype
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task DistributionsCode0
Learning Differential Operators for Interpretable Time Series Modeling—0
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
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails—0
Quantum Multi-Agent Meta Reinforcement Learning—0
Meta Learning for High-dimensional Ising Model Selection Using _1-regularized Logistic Regression—0
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition—0
IPNET:Influential Prototypical Networks for Few Shot Learning—0
CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks—0
Meta-Learning Online Control for Linear Dynamical Systems—0
Meta Sparse Principal Component Analysis—0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning—0
Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning—0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—0
GEDI: A Graph-based End-to-end Data Imputation Framework—0
Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation—0
Intelligent MIMO Detection Using Meta Learning—0
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine LearningCode0
A Game-Theoretic Perspective of Generalization in Reinforcement 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