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 10011010 of 3569 papers

TitleStatusHype
Extreme Algorithm Selection With Dyadic Feature RepresentationCode0
Learning advisor networks for noisy image classificationCode0
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-LearningCode0
A Stylometric Inquiry into Hyperpartisan and Fake NewsCode0
Data-Driven Performance Guarantees for Classical and Learned OptimizersCode0
Data-driven Meta-set Based Fine-Grained Visual ClassificationCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Multi-task Maximum Entropy Inverse Reinforcement LearningCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Show:102550
← PrevPage 101 of 357Next →

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