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 801825 of 3569 papers

TitleStatusHype
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding0
Subspace Adaptation Prior for Few-Shot LearningCode0
In-Context Learning for Few-Shot Molecular Property Prediction0
Learn From Model Beyond Fine-Tuning: A SurveyCode1
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters0
Neural Relational Inference with Fast Modular Meta-learningCode1
Federated Multi-Level Optimization over Decentralized Networks0
Self-Supervised Dataset Distillation for Transfer LearningCode1
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
Early Warning Prediction with Automatic Labeling in Epilepsy Patients0
A Meta-Learning Perspective on Transformers for Causal Language Modeling0
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective0
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels0
Federated Conditional Stochastic Optimization0
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-LearningCode0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm0
On the Role of Neural Collapse in Meta Learning Models for Few-shot LearningCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples0
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems0
Domain Adaptive Few-Shot Open-Set LearningCode1
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning0
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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