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 726750 of 3569 papers

TitleStatusHype
Learning to Learn from Noisy Labeled DataCode0
Bayesian Meta Sampling for Fast Uncertainty AdaptationCode0
Meta Architecture SearchCode0
Learning to learn ecosystems from limited data -- a meta-learning approachCode0
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
Learning to learn by gradient descent by gradient descentCode0
Learning to Generate Noise for Multi-Attack RobustnessCode0
Learning to Learn By Self-CritiqueCode0
Learning to Forget for Meta-LearningCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Learning to Learn Cropping Models for Different Aspect Ratio RequirementsCode0
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement LearningCode0
Base Models for Parabolic Partial Differential EquationsCode0
Learning to Evolve on Dynamic GraphsCode0
Learning to Design RNACode0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain GeneralizationCode0
Learning to Defer to a Population: A Meta-Learning ApproachCode0
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)Code0
Learning to Demodulate from Few Pilots via Offline and Online Meta-LearningCode0
Learning to Explore for Stochastic Gradient MCMCCode0
Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model GeneralizationCode0
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution TasksCode0
A Meta-Learning Approach to Bayesian Causal DiscoveryCode0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
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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