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 1776–1800 of 3569 papers

TitleStatusHype
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal PredictionCode0
Digital Twin-Empowered Network Planning for Multi-Tier Computing—0
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-LearningCode0
Meta-Ensemble Parameter Learning—0
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Meta-Learning Priors for Safe Bayesian Optimization—0
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation—0
PersA-FL: Personalized Asynchronous Federated Learning—0
On Stability and Generalization of Bilevel Optimization Problem—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
Recurrent Meta-Learning against Generalized Cold-start Problem in CTR PredictionCode0
Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification—0
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes—0
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases—0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
Beyond Bayes-optimality: meta-learning what you know you don't know—0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
The Curse of Unrolling: Rate of Differentiating Through Optimization—0
Meta-Learning a Cross-lingual Manifold for Semantic ParsingCode0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression—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