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
Procedural Generalization by Planning with Self-Supervised World Models—0
Progressive Conservative Adaptation for Evolving Target Domains—0
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning—0
Projective Subspace Networks For Few-Shot Learning—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Prompted Meta-Learning for Few-shot Knowledge Graph Completion—0
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds—0
Prototype-Guided Memory Replay for Continual Learning—0
Provable Generalization of Overparameterized Meta-learning Trained with SGD—0
Provable Hierarchy-Based Meta-Reinforcement Learning—0
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback—0
Provably Efficient Model-based Policy Adaptation—0
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning—0
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach—0
Pushing the Boundary: Specialising Deep Configuration Performance Learning—0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms—0
qNBO: quasi-Newton Meets Bilevel Optimization—0
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines—0
Quantum Multi-Agent Meta Reinforcement Learning—0
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization—0
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain—0
RankAdaptor: Hierarchical Rank Allocation for Efficient Fine-Tuning Pruned LLMs via Performance Model—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