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 1681–1690 of 3569 papers

TitleStatusHype
Selective classification using a robust meta-learning approach—0
Instance-Conditional Timescales of Decay for Non-Stationary Learning—0
Task-Adaptive Meta-Learning Framework for Advancing Spatial GeneralizabilityCode0
General-Purpose In-Context Learning by Meta-Learning TransformersCode0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
Few-Shot Preference Learning for Human-in-the-Loop RL—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
Meta-Learning Fast Weight Language Models—0
Meta Learning for Few-Shot Medical Text Classification—0
Adaptive Robust Model Predictive Control via Uncertainty Cancellation—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