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 151–175 of 3569 papers

TitleStatusHype
Meta-Learning Neural Mechanisms rather than Bayesian Priors—0
MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties—0
LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding—0
UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data—0
Riemannian Geometric-based Meta Learning—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
Meta-learning characteristics and dynamics of quantum systems—0
Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study—0
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse TasksCode0
Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification—0
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and ApplicationsCode9
PIED: Physics-Informed Experimental Design for Inverse ProblemsCode0
MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning—0
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee—0
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
Meta Learning not to Learn: Robustly Informing Meta-Learning under Nuisance-Varying Families—0
On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness—0
Meta-Learning to Explore via Memory Density Feedback—0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Category-level Meta-learned NeRF Priors for Efficient Object Mapping—0
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection—0
PABBO: Preferential Amortized Black-Box OptimizationCode0
Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective—0
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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