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

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Showing 1901–1925 of 3569 papers

TitleStatusHype
Online Meta-learning for AutoML in Real-time (OnMAR)—0
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction—0
Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation—0
Online Meta-Learning in Adversarial Multi-Armed Bandits—0
No-regret Non-convex Online Meta-Learning—0
Online Nonconvex Bilevel Optimization with Bregman Divergences—0
Online Structured Meta-learning—0
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation—0
On Meta-Learning for Dynamic Ensemble Selection—0
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level—0
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles—0
A Distribution-Dependent Analysis of Meta-Learning—0
On Parameter Tuning in Meta-learning for Computer Vision—0
On Stability and Generalization of Bilevel Optimization Problem—0
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness—0
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games—0
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms—0
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies—0
On the cross-lingual transferability of multilingual prototypical models across NLU tasks—0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning—0
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness—0
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting—0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—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