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 926950 of 3569 papers

TitleStatusHype
Image Deformation Meta-Networks for One-Shot LearningCode0
Learning How to Demodulate from Few Pilots via Meta-LearningCode0
Learning One-Shot Imitation from Humans without HumansCode0
Deep Learning Theory Review: An Optimal Control and Dynamical Systems PerspectiveCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Learning advisor networks for noisy image classificationCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Deep Compressed SensingCode0
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance EstimationCode0
Learning Fast Adaptation with Meta Strategy OptimizationCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal ResourcesCode0
A Meta-Transfer Objective for Learning to Disentangle Causal MechanismsCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
Decoder Choice Network for Meta-LearningCode0
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based TrainingCode0
Episodic Multi-Task Learning with Heterogeneous Neural ProcessesCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Deciphering Trajectory-Aided LLM Reasoning: An Optimization PerspectiveCode0
Asynchronous Distributed Bilevel OptimizationCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning to Continually Learn Rapidly from Few and Noisy DataCode0
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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