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 30513075 of 3569 papers

TitleStatusHype
Model Selection for Cross-Lingual TransferCode0
Learning to Self-Train for Semi-Supervised Few-Shot ClassificationCode0
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task DivisionCode0
Episodic Multi-Task Learning with Heterogeneous Neural ProcessesCode0
Multi-task Meta Label Correction for Time Series PredictionCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based TrainingCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Centroids Matching: an efficient Continual Learning approach operating in the embedding spaceCode0
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patientsCode0
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-TimeCode0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Assessor-Guided Learning for Continual EnvironmentsCode0
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
Joint inference and input optimization in equilibrium networksCode0
More Flexible PAC-Bayesian Meta-Learning by Learning Learning AlgorithmsCode0
MVDG: A Unified Multi-view Framework for Domain GeneralizationCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
ST-SQL: Semi-Supervised Self-Training for Text-to-SQL via Column Specificity Meta-LearningCode0
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?Code0
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal ResourcesCode0
Enabling Systematic Generalization in Abstract Spatial Reasoning through Meta-Learning for CompositionalityCode0
A Simple Neural Attentive Meta-LearnerCode0
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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