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 2826–2850 of 3569 papers

TitleStatusHype
Proxy Network for Few Shot LearningCode0
Information Theoretic Meta Learning with Gaussian Processes—0
A Survey on Machine Learning from Few Samples—0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—0
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
Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning—0
A Preliminary Study on Leveraging Meta Learning Technique for Code-switching Speech Recognition—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
learn2learn: A Library for Meta-Learning ResearchCode0
Meta-Learning with Shared Amortized Variational InferenceCode0
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework—0
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
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization—0
Adaptive Hierarchical Hyper-gradient Descent—0
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations—0
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling—0
Meta Feature Modulator for Long-tailed Recognition—0
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles—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