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

Showing 10011025 of 3569 papers

TitleStatusHype
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation BatteriesCode0
A Stylometric Inquiry into Hyperpartisan and Fake NewsCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Learning Fast Adaptation with Meta Strategy OptimizationCode0
Theoretical Convergence of Multi-Step Model-Agnostic Meta-LearningCode0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Natural Language to Structured Query Generation via Meta-LearningCode0
Data-Driven Performance Guarantees for Classical and Learned OptimizersCode0
Data-driven Meta-set Based Fine-Grained Visual ClassificationCode0
Learning advisor networks for noisy image classificationCode0
learn2learn: A Library for Meta-Learning ResearchCode0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Structured Prediction for Conditional Meta-LearningCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Adaptive Conditional Quantile Neural ProcessesCode0
A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded SmoothnessCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Curriculum Meta-Learning for Few-shot ClassificationCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Latent Task-Specific Graph Network SimulatorsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified