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 2376–2400 of 3569 papers

TitleStatusHype
Unsupervised Meta-Learning via In-Context Learning—0
Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
Using a thousand optimization tasks to learn hyperparameter search strategies—0
Using learned optimizers to make models robust to input noise—0
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response—0
Using Sensory Time-cue to enable Unsupervised Multimodal Meta-learning—0
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES—0
Bayesian Meta-reinforcement Learning for Traffic Signal Control—0
Variable-Shot Adaptation for Incremental Meta-Learning—0
Variable-Shot Adaptation for Online Meta-Learning—0
Variadic Learning by Bayesian Nonparametric Deep Embedding—0
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks—0
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
Variational Continual Bayesian Meta-Learning—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
VIABLE: Fast Adaptation via Backpropagating Learned Loss—0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Virtual Node Tuning for Few-shot Node Classification—0
Vision transformers in domain adaptation and domain generalization: a study of robustness—0
Visual Goal-Directed Meta-Learning with Contextual Planning Networks—0
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning—0
Visual Question Answering as a Meta Learning Task—0
ViSymRe: Vision-guided Multimodal Symbolic Regression—0
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions—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