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 1551–1575 of 3569 papers

TitleStatusHype
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Learning-to-Learn Personalised Human Activity Recognition Models—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Principled Acceleration of Iterative Numerical Methods Using Machine Learning—0
Learning a Meta-Solver for Syntax-Guided Program Synthesis—0
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation—0
Learning Attentive Meta-Transfer—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
Learning to Learn Neural Networks—0
Learning Differential Operators for Interpretable Time Series Modeling—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Fully Online Meta-Learning Without Task Boundaries—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy—0
Contextual HyperNetworks for Novel Feature Adaptation—0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes—0
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning—0
Reconstruction guided Meta-learning for Few Shot Open Set Recognition—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning—0
Learning-to-learn non-convex piecewise-Lipschitz functions—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—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