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 2001–2025 of 3569 papers

TitleStatusHype
Teaching to Learn: Sequential Teaching of Agents with Inner States—0
Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection—0
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks—0
Temporal Variational Implicit Neural Representations—0
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning—0
Test-Time Adaptation for Generalizable Task Progress Estimation—0
Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning—0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach—0
Thalamocortical contribution to solving credit assignment in neural systems—0
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning—0
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems—0
The Bayesian Approach to Continual Learning: An Overview—0
The broader spectrum of in-context learning—0
The challenge of uncertainty quantification of large language models in medicine—0
The Context-Aware Learner—0
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence—0
The Curse of Unrolling: Rate of Differentiating Through Optimization—0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence—0
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification—0
The effects of negative adaptation in Model-Agnostic Meta-Learning—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning—0
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine 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