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 1851–1875 of 3569 papers

TitleStatusHype
Safe Reinforcement Learning through Meta-learned Instincts—0
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming—0
SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization—0
Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning—0
Sample Efficient Adaptive Text-to-Speech—0
Sample Efficient Linear Meta-Learning by Alternating Minimization—0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning—0
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning—0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning—0
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning—0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
Scalable Meta-Learning via Mixed-Mode Differentiation—0
Scalable Meta-Learning with Gaussian Processes—0
Scaling Opponent Shaping to High Dimensional Games—0
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space—0
Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach—0
Search-based Optimisation of LLM Learning Shots for Story Point Estimation—0
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction—0
Seeded self-play for language learning—0
Seeker based Adaptive Guidance via Reinforcement Meta-Learning Applied to Asteroid Close Proximity Operations—0
SeFENet: Robust Deep Homography Estimation via Semantic-Driven Feature Enhancement—0
Selective classification using a robust meta-learning approach—0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—0
Self-Configuring nnU-Nets Detect Clouds in Satellite Images—0
Self-Domain Adaptation for Face Anti-Spoofing—0
Show:102550
← PrevPage 75 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