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 701725 of 3569 papers

TitleStatusHype
Learning to Learn Words from Visual ScenesCode0
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual LearningCode0
Learning to Learn Variational Semantic MemoryCode0
Learning to Multi-Task by Active SamplingCode0
Learning to Learn Kernels with Variational Random FeaturesCode0
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiersCode0
Learning to Learn Single Domain GeneralizationCode0
Learning to Learn from Noisy Labeled DataCode0
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
Learning to Learn Cropping Models for Different Aspect Ratio RequirementsCode0
Analyzing the Effectiveness of Quantum Annealing with Meta-LearningCode0
Learning to learn ecosystems from limited data -- a meta-learning approachCode0
Learning to learn by gradient descent by gradient descentCode0
A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying ModerationCode0
Learning to Learn By Self-CritiqueCode0
Adaptive Prior Selection for Repertoire-based Online Adaptation in RoboticsCode0
Learning to Generate Noise for Multi-Attack RobustnessCode0
A Meta-Learning Framework for Generalized Zero-Shot LearningCode0
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text GenerationCode0
Adaptive Posterior Learning: few-shot learning with a surprise-based memory moduleCode0
Learning to Forget for Meta-LearningCode0
Been There, Done That: Meta-Learning with Episodic RecallCode0
BayesPCN: A Continually Learnable Predictive Coding Associative MemoryCode0
Learning to Learn Transferable AttackCode0
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement 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