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 591–600 of 3569 papers

TitleStatusHype
Learning-to-Learn the Wave Angle Estimation—0
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study—0
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent—0
NTK-Guided Few-Shot Class Incremental LearningCode1
Improving Generalization via Meta-Learning on Hard Samples—0
Compositional learning of functions in humans and machines—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology—0
Meta Operator for Complex Query Answering on Knowledge Graphs—0
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning—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