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
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
Darwin Godel Machine: Open-Ended Evolution of Self-Improving AgentsCode5
RecBole 2.0: Towards a More Up-to-Date Recommendation LibraryCode4
Adversarial Cheap TalkCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Discovered Policy OptimisationCode3
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
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse ProblemsCode2
Learning Deep Time-index Models for Time Series ForecastingCode2
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?Code2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
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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