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 811–820 of 3569 papers

TitleStatusHype
Early Warning Prediction with Automatic Labeling in Epilepsy Patients—0
A Meta-Learning Perspective on Transformers for Causal Language Modeling—0
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective—0
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems—0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-LearningCode0
Federated Conditional Stochastic Optimization—0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm—0
On the Role of Neural Collapse in Meta Learning Models for Few-shot LearningCode0
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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