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 1931–1940 of 3569 papers

TitleStatusHype
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
Show:102550
← PrevPage 194 of 357Next →
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