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 14911500 of 3569 papers

TitleStatusHype
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning0
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning0
Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation0
Bi-Level Meta-Learning for Few-Shot Domain Generalization0
A Meta-Reinforcement Learning Approach to Process Control0
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles0
Bilevel Continual Learning0
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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