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 3111–3120 of 3569 papers

TitleStatusHype
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Efficient Optimization of Loops and Limits with Randomized Telescoping SumsCode0
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-LearningCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Regression Networks for Meta-Learning Few-Shot ClassificationCode0
Rethinking of Encoder-based Warm-start Methods in Hyperparameter OptimizationCode0
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-LearningCode0
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward ModelsCode0
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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