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 3151–3200 of 3569 papers

TitleStatusHype
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
Learning Fast Adaptation with Meta Strategy OptimizationCode0
Improving Federated Learning Personalization via Model Agnostic Meta LearningCode0
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model—0
A Theoretical Analysis of the Number of Shots in Few-Shot Learning—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Data Valuation using Reinforcement LearningCode0
Efficient meta reinforcement learning via meta goal generation—0
Few-Shot Regression via Learning Sparsifying Basis Functions—0
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph CompletionCode0
Localized Generations with Deep Neural Networks for Multi-Scale Structured Datasets—0
Decoder Choice Network for Meta-LearningCode0
Semi-Supervised Few-Shot Learning with a Controlled Degree of Task-Adaptive Conditioning—0
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning—0
Role of two learning rates in convergence of model-agnostic meta-learning—0
NORML: Nodal Optimization for Recurrent Meta-Learning—0
Meta-Learning for Variational Inference—0
ES-MAML: Simple Hessian-Free Meta LearningCode0
A Data-Efficient Mutual Information Neural Estimator for Statistical Dependency Testing—0
Meta-Learning Runge-Kutta—0
Meta Learning via Learned Loss—0
Hidden incentives for self-induced distributional shift—0
Meta-RCNN: Meta Learning for Few-Shot Object Detection—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior—0
Discrete InfoMax Codes for Meta-Learning—0
Meta-Learning with Network Pruning for Overfitting Reduction—0
Meta-Learning by Hallucinating Useful Examples—0
Meta-NeighborhoodsCode0
MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization—0
Meta Reinforcement Learning for Sim-to-real Domain Adaptation—0
Learning to Tune XGBoost with XGBoost—0
Torchmeta: A Meta-Learning library for PyTorchCode0
Meta-Learning for Few-Shot Time Series Classification—0
Modular Meta-Learning with Shrinkage—0
Differentially Private Meta-Learning—0
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony—0
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning—0
Recognizing Variables from their Data via Deep Embeddings of Distributions—0
Learning to Propagate for Graph Meta-LearningCode0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Cellular neuromodulation in artificial networks—0
A Meta-Learning Framework for Generalized Zero-Shot LearningCode0
Meta-learnt priors slow down catastrophic forgetting in neural networksCode0
Learning to Sample: an Active Learning Framework—0
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification—0
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit FeedbackCode0
AutoML for Contextual Bandits—0
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
Meta Learning with Relational Information for Short SequencesCode0
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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