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 751–775 of 3569 papers

TitleStatusHype
Latent Task-Specific Graph Network SimulatorsCode0
When Meta-Learning Meets Online and Continual Learning: A Survey—0
Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces—0
Massive Editing for Large Language Models via Meta LearningCode1
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
Learning to Learn for Few-shot Continual Active Learning—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning ApplicationsCode1
Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis—0
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?—0
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept AcquisitionCode0
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval—0
Investigating Relative Performance of Transfer and Meta Learning—0
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction—0
Meta Learning for Multi-View Visuomotor Systems—0
Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical SystemsCode0
A Survey on Knowledge Editing of Neural Networks—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
Meta-Learning Strategies through Value Maximization in Neural Networks—0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
Episodic Multi-Task Learning with Heterogeneous Neural ProcessesCode0
On Training Implicit Meta-Learning With Applications to Inductive Weighing in Consistency Regularization—0
Contextual Stochastic Bilevel Optimization—0
CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter—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