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 101–125 of 3569 papers

TitleStatusHype
Meta-Learning in Self-Play Regret Minimization—0
Approximating Nash Equilibria in General-Sum Games via Meta-Learning—0
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings—0
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving—0
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design—0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining—0
MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications—0
Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life PredictionCode1
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration—0
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning—0
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems—0
Manifold meta-learning for reduced-complexity neural system identificationCode0
Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML DatasetCode0
Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting—0
Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality—0
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization—0
Meta-Continual Learning of Neural Fields—0
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link PredictionCode0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
The challenge of uncertainty quantification of large language models in medicine—0
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints—0
A Classification View on Meta Learning Bandits—0
iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning—0
Towards An Efficient and Effective En Route Travel Time Estimation FrameworkCode0
Meta-Learning Driven Movable-Antenna-assisted Full-Duplex RSMA for Multi-User Communication: Performance and Optimization—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