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 51–75 of 3569 papers

TitleStatusHype
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person RetrievalCode1
Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life PredictionCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box OptimizationCode1
Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature LearningCode1
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real UsersCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
MedFuncta: Modality-Agnostic Representations Based on Efficient Neural FieldsCode1
LoRA Recycle: Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAsCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Personalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-LearningCode1
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAsCode1
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Amortized Probabilistic Conditioning for Optimization, Simulation and InferenceCode1
Metalic: Meta-Learning In-Context with Protein Language ModelsCode1
PersonalLLM: Tailoring LLMs to Individual PreferencesCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Nonrigid Reconstruction of Freehand Ultrasound without a TrackerCode1
Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervisionCode1
Pairwise Difference Learning for ClassificationCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Meta-Learning Loss Functions for Deep Neural NetworksCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Show:102550
← PrevPage 3 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