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 951–975 of 3569 papers

TitleStatusHype
Transfer Learning for Finetuning Large Language Models—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning—0
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling—0
Hyperparameter Optimization in Machine Learning—0
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta LearningCode0
Meta-Learning Adaptable Foundation Models—0
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments—0
Meta-Learning Approaches for Improving Detection of Unseen Speech Deepfakes—0
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Meta-Learning with Heterogeneous Tasks—0
Gradient-Based Meta Learning for Uplink RSMA with Beyond Diagonal RIS—0
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks—0
SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer—0
Integrated Image-Text Based on Semi-supervised Learning for Small Sample Instance Segmentation—0
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning—0
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization—0
Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning—0
Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware SubspaceCode0
Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines—0
Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach—0
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation—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