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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2226–2250 of 3569 papers

TitleStatusHype
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
Dynamic population-based meta-learning for multi-agent communication with natural language—0
Revisit Multimodal Meta-Learning through the Lens of Multi-Task LearningCode0
Accelerating Gradient-based Meta Learner—0
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware—0
Meta-Learning for Multi-Label Few-Shot Classification—0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
MIGS: Meta Image Generation from Scene GraphsCode0
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
On Hard Episodes in Meta-Learning—0
Contextual Gradient Scaling for Few-Shot LearningCode0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Bayesian Active Meta-Learning for Black-Box Optimization—0
Learning Prototype-oriented Set Representations for Meta-Learning—0
Provable Hierarchy-Based Meta-Reinforcement Learning—0
Learning to Learn a Cold-start Sequential Recommender—0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Meta-Learning with Adjoint Methods—0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling—0
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty—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