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 851–875 of 3569 papers

TitleStatusHype
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
qNBO: quasi-Newton Meets Bilevel Optimization—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network—0
Meta-learning of shared linear representations beyond well-specified linear regression—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
In-Context Meta LoRA Generation—0
ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning—0
Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management—0
Tailored Forecasting from Short Time Series via Meta-learning—0
Evaluating Data Influence in Meta Learning—0
Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction PredictionCode0
Towards Sharper Information-theoretic Generalization Bounds for Meta-Learning—0
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
A real-time battle situation intelligent awareness system based on Meta-learning & RNN—0
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning—0
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization—0
One-Class Domain Adaptation via Meta-Learning—0
Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning—0
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning—0
Show:102550
← PrevPage 35 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