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

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Showing 3001–3050 of 3569 papers

TitleStatusHype
Learning Fast Adaptation with Meta Strategy OptimizationCode0
MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelinesCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Clustering Indices based Automatic Classification Model SelectionCode0
MeLU: Meta-Learned User Preference Estimator for Cold-Start RecommendationCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Learning advisor networks for noisy image classificationCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Evaluating Meta-Feature Selection for the Algorithm Recommendation ProblemCode0
A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?Code0
Active exploration in parameterized reinforcement learningCode0
ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuningCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Model-agnostic Measure of Generalization DifficultyCode0
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
MetaAdvDet: Towards Robust Detection of Evolving Adversarial AttacksCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
MetaAge: Meta-Learning Personalized Age EstimatorsCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Latent Task-Specific Graph Network SimulatorsCode0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
Regularized Fine-grained Meta Face Anti-spoofingCode0
Closed-form Sample Probing for Learning Generative Models in Zero-shot LearningCode0
3FM: Multi-modal Meta-learning for Federated TasksCode0
MetaASSIST: Robust Dialogue State Tracking with Meta LearningCode0
Unsupervised Learning for Combinatorial Optimization Needs Meta-LearningCode0
Learning to Learn without Forgetting using AttentionCode0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Associative Alignment for Few-shot Image ClassificationCode0
MetaAug: Meta-Data Augmentation for Post-Training QuantizationCode0
Classical Sequence Match is a Competitive Few-Shot One-Class LearnerCode0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Rethinking Task Sampling for Few-shot Vision-Language Transfer LearningCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Reinforcement Learning In Two Player Zero Sum Simultaneous Action GamesCode0
Memory Efficient Neural Processes via Constant Memory Attention BlockCode0
Reinforcement learning to learn quantum states for Heisenberg scaling accuracyCode0
ES-MAML: Simple Hessian-Free Meta LearningCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Modeling and Optimization Trade-off in Meta-learningCode0
Chameleon: Learning Model Initializations Across Tasks With Different SchemasCode0
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
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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