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 1901–1950 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
On the ERM Principle in Meta-Learning—0
On-the-Fly Adaptation of Source Code Models using Meta-Learning—0
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification—0
On the Generalization Error of Meta Learning for the Gibbs Algorithm—0
On the Generalization of Neural Combinatorial Optimization Heuristics—0
On the Global Optimality of Model-Agnostic Meta-Learning—0
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification—0
On the Influence of Masking Policies in Intermediate Pre-training—0
On the Iteration Complexity of Hypergradient Computations—0
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization—0
On the Practical Consistency of Meta-Reinforcement Learning Algorithms—0
On the Subspace Structure of Gradient-Based Meta-Learning—0
On Training Implicit Meta-Learning With Applications to Inductive Weighing in Consistency Regularization—0
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification—0
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis—0
Open Domain Generalization with Domain-Augmented Meta-Learning—0
Optimal allocation of data across training tasks in meta-learning—0
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization—0
Acceleration in Policy Optimization—0
Optimistic Meta-Gradients—0
Optimization of Lightweight Malware Detection Models For AIoT Devices—0
Optimized Generic Feature Learning for Few-shot Classification across Domains—0
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach—0
Optimizing quantum heuristics with meta-learning—0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems—0
Show:102550
← PrevPage 39 of 72Next →
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