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 Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework—0
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots—0
Efficient Gaussian Neural Processes for Regression—0
Efficient Federated Meta-Learning over Multi-Access Wireless Networks—0
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes—0
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization—0
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction—0
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction—0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Efficient Collective Entity Linking with Stacking—0
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
Bayesian Online Meta-Learning—0
A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access—0
Adaptive Physics-informed Neural Networks: A Survey—0
Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks—0
Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation—0
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting—0
A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming—0
Effective Meta-Regularization by Kernelized Proximal Regularization—0
Effective Bilevel Optimization via Minimax Reformulation—0
EEML: Ensemble Embedded Meta-learning—0
A Meta-learning based Generalizable Indoor Localization Model using Channel State Information—0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks—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