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 1851–1860 of 3569 papers

TitleStatusHype
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Negative Inner-Loop Learning Rates Learn Universal Features—0
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent—0
Neural Algorithms for Graph Navigation—0
Neural Collaborative Filtering Bandits via Meta Learning—0
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning—0
Neural Variational Dropout Processes—0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning—0
Neuromorphic on-chip reservoir computing with spiking neural network architectures—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