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 2651–2675 of 3569 papers

TitleStatusHype
Information Theoretic Meta Learning with Gaussian Processes—0
A Survey on Machine Learning from Few Samples—0
Grounded Language Learning Fast and SlowCode1
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—0
Simulating Unknown Target Models for Query-Efficient Black-box AttacksCode1
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—0
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily—0
A Preliminary Study on Using Meta-learning Technique for Information Retrieval—0
A Preliminary Study on Leveraging Meta Learning Technique for Code-switching Speech Recognition—0
Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning—0
A Meta-Learning Control Algorithm with Provable Finite-Time Guarantees—0
Short-term Traffic Prediction with Deep Neural Networks: A Survey—0
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation—0
Meta-Learning with Shared Amortized Variational InferenceCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework—0
Transductive Information Maximization For Few-Shot LearningCode1
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning—0
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
BOIL: Towards Representation Change for Few-shot LearningCode1
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization—0
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Adaptive Hierarchical Hyper-gradient Descent—0
Show:102550
← PrevPage 107 of 143Next →
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