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 1426–1450 of 3569 papers

TitleStatusHype
Imputation of missing values in multi-view data—0
In-Context In-Context Learning with Transformer Neural Processes—0
In-Context Learning for Few-Shot Molecular Property Prediction—0
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
In-context learning of evolving data streams with tabular foundational models—0
Generalizable Person Re-Identification by Domain-Invariant Mapping Network—0
Continuous Learning in a Hierarchical Multiscale Neural Network—0
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment—0
Incremental Few-Shot Object Detection—0
Generalizable Neural Fields as Partially Observed Neural Processes—0
Incremental Meta-Learning via Indirect Discriminant Alignment—0
Meta-Learning Mean Functions for Gaussian Processes—0
Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization—0
Indic Languages Automatic Speech Recognition using Meta-Learning Approach—0
Inductive Linear Probing for Few-shot Node Classification—0
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning—0
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning—0
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning—0
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study—0
Information-Aware Time Series Meta-Contrastive Learning—0
Continual Quality Estimation with Online Bayesian Meta-Learning—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