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 126–150 of 3569 papers

TitleStatusHype
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
The Self-Learning Agent with a Progressive Neural Network Integrated Transformer—0
Efficient Model Selection for Time Series Forecasting via LLMs—0
Sparse Gaussian Neural ProcessesCode0
Enabling Systematic Generalization in Abstract Spatial Reasoning through Meta-Learning for CompositionalityCode0
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization—0
Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation—0
Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization—0
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data—0
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI RecommendationCode0
Reward Design for Reinforcement Learning AgentsCode0
PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box OptimizationCode1
Flow to Learn: Flow Matching on Neural Network Parameters—0
Geometric Meta-Learning via Coupled Ricci Flow: Unifying Knowledge Representation and Quantum Entanglement—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Learning to segment anatomy and lesions from disparately labeled sources in brain MRI—0
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain GeneralizationCode0
Adaptive Physics-informed Neural Networks: A Survey—0
Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature LearningCode1
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes—0
GenMetaLoc: Learning to Learn Environment-Aware Fingerprint Generation for Sample Efficient Wireless Localization—0
HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks—0
Show:102550
← PrevPage 6 of 143Next →
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