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 1776–1800 of 3569 papers

TitleStatusHype
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback—0
Provably Efficient Model-based Policy Adaptation—0
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning—0
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach—0
Pushing the Boundary: Specialising Deep Configuration Performance Learning—0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms—0
qNBO: quasi-Newton Meets Bilevel Optimization—0
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines—0
Quantum Multi-Agent Meta Reinforcement Learning—0
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization—0
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain—0
RankAdaptor: Hierarchical Rank Allocation for Efficient Fine-Tuning Pruned LLMs via Performance Model—0
Ranking architectures using meta-learning—0
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines—0
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning—0
Rapid Model Architecture Adaption for Meta-Learning—0
RARD II: The 94 Million Related-Article Recommendation Dataset—0
Rate-optimal Meta Learning of Classification Error—0
Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments—0
Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning—0
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes—0
Recognizing Variables from their Data via Deep Embeddings of Distributions—0
Recommending Learning Algorithms and Their Associated Hyperparameters—0
Recovering Time-Varying Networks From Single-Cell Data—0
Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis—0
Show:102550
← PrevPage 72 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