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 1926–1950 of 3569 papers

TitleStatusHype
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
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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