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

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Showing 1–50 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
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
RecBole 2.0: Towards a More Up-to-Date Recommendation LibraryCode4
Adversarial Cheap TalkCode3
Discovered Policy OptimisationCode3
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box OptimizationCode2
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-LearningCode2
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with TransformersCode2
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-OptimizationCode2
pyhgf: A neural network library for predictive codingCode2
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-ImprovementCode2
A Practitioner's Guide to Continual Multimodal PretrainingCode2
NAVIX: Scaling MiniGrid Environments with JAXCode2
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse ProblemsCode2
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
Online Adaptation of Language Models with a Memory of Amortized ContextsCode2
VRP-SAM: SAM with Visual Reference PromptCode2
Learning a Decision Tree Algorithm with TransformersCode2
Learning Universal PredictorsCode2
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAXCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
Transformers learn in-context by gradient descentCode2
MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized AdaptationCode2
Discovering Evolution Strategies via Meta-Black-Box OptimizationCode2
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation ExploitationCode2
Learning Deep Time-index Models for Time Series ForecastingCode2
Towards Learning Universal Hyperparameter Optimizers with TransformersCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
Neural-Fly Enables Rapid Learning for Agile Flight in Strong WindsCode2
Decomposed Meta-Learning for Few-Shot Named Entity RecognitionCode2
Learning What Not to Segment: A New Perspective on Few-Shot SegmentationCode2
Tutorial on amortized optimizationCode2
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series DataCode2
Efficient and Modular Implicit DifferentiationCode2
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?Code2
Meta-Learning Symmetries by ReparameterizationCode2
Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural NetsCode2
Frustratingly Simple Few-Shot Object DetectionCode2
TensorFlow Quantum: A Software Framework for Quantum Machine LearningCode2
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement LearningCode2
Generalized Inner Loop Meta-LearningCode2
Prototypical Networks for Few-shot LearningCode2
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
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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