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 1531–1540 of 3569 papers

TitleStatusHype
Meta Representation Learning with Contextual Linear Bandits—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectsCode0
Deep Learning with Label Noise: A Hierarchical Approach—0
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge TransferCode1
Meta-Learning Adversarial Bandits—0
Towards Learning Universal Hyperparameter Optimizers with TransformersCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
Learning a Better Initialization for Soft Prompts via 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