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 125 of 3569 papers

TitleStatusHype
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and ApplicationsCode9
Darwin Godel Machine: Open-Ended Evolution of Self-Improving AgentsCode5
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
RecBole 2.0: Towards a More Up-to-Date Recommendation LibraryCode4
Discovered Policy OptimisationCode3
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Adversarial Cheap TalkCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Generalized Inner Loop Meta-LearningCode2
Frustratingly Simple Few-Shot Object DetectionCode2
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with TransformersCode2
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-ImprovementCode2
Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural NetsCode2
Learning a Decision Tree Algorithm with TransformersCode2
Discovering Evolution Strategies via Meta-Black-Box OptimizationCode2
Decomposed Meta-Learning for Few-Shot Named Entity RecognitionCode2
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse ProblemsCode2
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?Code2
Learning Deep Time-index Models for Time Series ForecastingCode2
A Practitioner's Guide to Continual Multimodal PretrainingCode2
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
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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