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

Showing 17711780 of 3569 papers

TitleStatusHype
Task-Robust Model-Agnostic Meta-Learning0
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment0
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News0
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios0
Distributed Representations of Words and Documents for Discriminating Similar Languages0
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data0
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application0
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds0
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified