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 30013025 of 3569 papers

TitleStatusHype
Learning Fast Adaptation with Meta Strategy OptimizationCode0
MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelinesCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Clustering Indices based Automatic Classification Model SelectionCode0
MeLU: Meta-Learned User Preference Estimator for Cold-Start RecommendationCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Learning advisor networks for noisy image classificationCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Evaluating Meta-Feature Selection for the Algorithm Recommendation ProblemCode0
A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?Code0
Active exploration in parameterized reinforcement learningCode0
ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuningCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Model-agnostic Measure of Generalization DifficultyCode0
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
MetaAdvDet: Towards Robust Detection of Evolving Adversarial AttacksCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
MetaAge: Meta-Learning Personalized Age EstimatorsCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Latent Task-Specific Graph Network SimulatorsCode0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
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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