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 32263250 of 3569 papers

TitleStatusHype
Robust Meta-Representation Learning via Global Label Inference and ClassificationCode0
Robust Molecular Property Prediction via Densifying Scarce Labeled DataCode0
Robustness of Meta Matrix Factorization Against Strict Privacy ConstraintsCode0
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary citiesCode0
Hierarchically Structured Meta-learningCode0
Diverse Preference Augmentation with Multiple Domains for Cold-start RecommendationsCode0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine LearningCode0
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain ValidationCode0
Discovering Weight Initializers with Meta LearningCode0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Differentiable plasticity: training plastic neural networks with backpropagationCode0
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph CompletionCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
On-device Online Learning and Semantic Management of TinyML SystemsCode0
Deep Mixture of Experts via Shallow EmbeddingCode0
Meta-Learning for Effective Multi-task and Multilingual ModellingCode0
Meta-Learning for Efficient Fine-Tuning of Large Language ModelsCode0
Meta Learning for Efficient Fine-Tuning of Large Language ModelsCode0
TAFE-Net: Task-Aware Feature Embeddings for Low Shot LearningCode0
Meta-learning for fast classifier adaptation to new users of Signature Verification systemsCode0
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency ParsingCode0
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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