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

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Showing 2001–2050 of 3569 papers

TitleStatusHype
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-LearningCode1
Learning Fast Sample Re-weighting Without Reward Data—0
Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis—0
Weakly Supervised Few-Shot Segmentation Via Meta-Learning—0
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach—0
Prototype-Guided Memory Replay for Continual Learning—0
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
Few-shot Visual Relationship Co-localizationCode1
Continual learning under domain transfer with sparse synaptic bursting—0
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging—0
Adaptation-Agnostic Meta-TrainingCode0
Meta Self-Learning for Multi-Source Domain Adaptation: A BenchmarkCode1
CMML: Contextual Modulation Meta Learning for Cold-Start RecommendationCode0
Relational Embedding for Few-Shot ClassificationCode1
Efficient Gaussian Neural Processes for Regression—0
Fairness-Aware Online Meta-learning—0
Self-Supervised Video Representation Learning with Meta-Contrastive Network—0
Learning-to-learn non-convex piecewise-Lipschitz functions—0
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space—0
Multimodal Knowledge Learning for Named Entity Disambiguation—0
BERT Learns to Teach: Knowledge Distillation with Meta Learning—0
Is Nash Equilibrium Approximator Learnable?—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-LearningCode0
Curriculum Meta-Learning for Next POI Recommendation—0
Efficient Federated Meta-Learning over Multi-Access Wireless Networks—0
Learning to Segment Medical Images from Few-Shot Sparse LabelsCode0
Prototype Completion for Few-Shot LearningCode1
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future—0
The Role of Global Labels in Few-Shot Classification and How to Infer Them—0
Meta Gradient Adversarial AttackCode1
Generating Personalized Dialogue via Multi-Task Meta-Learning—0
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight TransformerCode1
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach—0
Multimodal Meta-Learning for Time Series Regression—0
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks—0
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation—0
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages—0
Meta-Learning for Few-Shot Named Entity Recognition—0
Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
Continual Quality Estimation with Online Bayesian Meta-Learning—0
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles—0
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse ParaphrasingCode1
Meta Learning and Its Applications to Natural Language Processing—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified