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 1–25 of 3569 papers

TitleStatusHype
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?—0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Imbalanced Regression Pipeline RecommendationCode0
Mixture of Experts in Large Language Models—0
Iceberg: Enhancing HLS Modeling with Synthetic DataCode0
Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks—0
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry DetectionCode0
The Bayesian Approach to Continual Learning: An Overview—0
A statistical physics framework for optimal learning—0
CHOMET: Conditional Handovers via Meta-Learning—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
Meta-Learning Transformers to Improve In-Context Generalization—0
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
High-Order Deep Meta-Learning with Category-Theoretic Interpretation—0
Automated Grading of Students' Handwritten Graphs: A Comparison of Meta-Learning and Vision-Large Language Models—0
MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations—0
Can Gradient Descent Simulate Prompting?—0
FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting—0
Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager—0
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
Distributionally robust minimization in meta-learning for system identification—0
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots—0
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based TrainingCode0
MetaQAP -- A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessment—0
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory—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