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 2951–2975 of 3569 papers

TitleStatusHype
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning—0
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning—0
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification—0
The Sample Complexity of Meta Sparse Regression—0
Learning to Continually LearnCode1
Few-shot acoustic event detection via meta-learning—0
Structured Prediction for Conditional Meta-LearningCode0
Meta-learning for mixed linear regression—0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
Meta Segmentation Network for Ultra-Resolution Medical Images—0
Personalized Federated Learning: A Meta-Learning Approach—0
Theoretical Convergence of Multi-Step Model-Agnostic Meta-LearningCode0
Few-Shot Few-Shot Learning and the role of Spatial Attention—0
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot TasksCode1
Meta-learning Extractors for Music Source SeparationCode1
Differentiable Bandit Exploration—0
Learning Adaptive Loss for Robust Learning with Noisy Labels—0
PACOH: Bayes-Optimal Meta-Learning with PAC-GuaranteesCode1
Task-Robust Model-Agnostic Meta-Learning—0
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement LearningCode0
On Parameter Tuning in Meta-learning for Computer Vision—0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Incremental Meta-Learning via Indirect Discriminant Alignment—0
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem—0
Meta-Learning across Meta-Tasks for Few-Shot 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