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 3201–3250 of 3569 papers

TitleStatusHype
Learning To Learn by Jointly Optimizing Neural Architecture and Weights—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks—0
Learning to Learn for Few-shot Continual Active Learning—0
Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
Learning to Learn Morphological Inflection for Resource-Poor Languages—0
Learning to Learn Neural Networks—0
Learning-to-learn non-convex piecewise-Lipschitz functions—0
Learning-to-Learn Personalised Human Activity Recognition Models—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Learning-to-Learn the Wave Angle Estimation—0
Learning to Learn to be Right for the Right Reasons—0
Learning to learn to communicate—0
Learning to Learn Transferable Generative Attack for Person Re-Identification—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Learning to Learn with Conditional Class Dependencies—0
Learning to Learn with Feedback and Local Plasticity—0
Learning to Learn with Indispensable Connections—0
Learning to Learn with Quantum Optimization via Quantum Neural Networks—0
Learning to Learn with Variational Information Bottleneck for Domain Generalization—0
Learning to Navigate the Web—0
Learning to Optimise General TSP Instances—0
Learning to Optimize on SPD Manifolds—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
Learning to Recommend via Meta Parameter Partition—0
Learning to Recover from Failures using Memory—0
Learning to Reinforcement Learn by Imitation—0
Learning to Remember from a Multi-Task Teacher—0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Learning to Sample: an Active Learning Framework—0
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs—0
Learning to segment anatomy and lesions from disparately labeled sources in brain MRI—0
Learning to Segment Skin Lesions from Noisy Annotations—0
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
Learning to Tune XGBoost with XGBoost—0
Learning to Unlearn for Robust Machine Unlearning—0
Learning to Update for Object Tracking with Recurrent Meta-learner—0
Learning Unsupervised Learning Rules—0
Learning via Surrogate PAC-Bayes—0
Learning where to learn—0
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications—0
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—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
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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