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 1751–1775 of 3569 papers

TitleStatusHype
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
Learning Online for Unified Segmentation and Tracking Models—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty—0
A Meta-GNN approach to personalized seizure detection and classification—0
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics—0
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes—0
Learn To Learn More Precisely—0
Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling—0
Distribution Embedding Network for Meta-Learning with Variable-Length Input—0
Learning Neural Processes on the Fly—0
Distributionally robust minimization in meta-learning for system identification—0
Learning Modality Knowledge Alignment for Cross-Modality Transfer—0
Task-Robust Model-Agnostic Meta-Learning—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment—0
Distributed Representations of Words and Documents for Discriminating Similar Languages—0
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News—0
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios—0
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application—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