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 3226–3250 of 3569 papers

TitleStatusHype
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta LearningCode0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition—0
Towards Understanding Generalization in Gradient-Based Meta-Learning—0
Adaptive Prior Selection for Repertoire-based Online Adaptation in RoboticsCode0
Meta-Learning for Black-box Optimization—0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
Seeker based Adaptive Guidance via Reinforcement Meta-Learning Applied to Asteroid Close Proximity Operations—0
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning—0
Few-Shot Representation Learning for Out-Of-Vocabulary WordsCode0
Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision—0
Two-stage Optimization for Machine Learning WorkflowCode0
Difficulty-aware Meta-learning for Rare Disease Diagnosis—0
Learning to Cope with Adversarial Attacks—0
Training an Interactive Helper—0
Meta-learning of textual representationsCode0
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing—0
Meta-learning Pseudo-differential Operators with Deep Neural Networks—0
Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks—0
Learning to Forget for Meta-LearningCode0
Meta-Learning via Learned Loss—0
Task Agnostic Continual Learning via Meta Learning—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
Few-Shot Learning with Per-Sample Rich Supervision—0
Meta-Learning Neural Bloom Filters—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