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 181–190 of 3569 papers

TitleStatusHype
On-device edge learning for IoT data streams: a survey—0
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—0
Enhancing CoMP-RSMA Performance with Movable Antennas: A Meta-Learning Optimization Framework—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
In-context learning of evolving data streams with tabular foundational models—0
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence—0
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning—0
MedFuncta: Modality-Agnostic Representations Based on Efficient Neural FieldsCode1
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
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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