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

Showing 31013125 of 3569 papers

TitleStatusHype
Resilient UAV Swarm Communications with Graph Convolutional Neural NetworkCode0
Torchmeta: A Meta-Learning library for PyTorchCode0
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR PredictionCode0
Submodular Meta-LearningCode0
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit FeedbackCode0
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal MechanismsCode0
Efficient time stepping for numerical integration using reinforcement learningCode0
Subspace Adaptation Prior for Few-Shot LearningCode0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
MetaFun: Meta-Learning with Iterative Functional UpdatesCode0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Efficient Optimization of Loops and Limits with Randomized Telescoping SumsCode0
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-LearningCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Regression Networks for Meta-Learning Few-Shot ClassificationCode0
Rethinking of Encoder-based Warm-start Methods in Hyperparameter OptimizationCode0
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-LearningCode0
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward ModelsCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Meta-GNN: On Few-shot Node Classification in Graph Meta-learningCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Theoretical Convergence of Multi-Step Model-Agnostic Meta-LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified