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 1226–1250 of 3569 papers

TitleStatusHype
Structural Knowledge-Driven Meta-Learning for Task Offloading in Vehicular Networks with Integrated Communications, Sensing and Computing—0
Informed Meta-Learning—0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
LiMAML: Personalization of Deep Recommender Models via Meta Learning—0
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond—0
Generalizing Reward Modeling for Out-of-Distribution Preference LearningCode0
On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic ChoicesCode0
Referee-Meta-Learning for Fast Adaptation of Locational Fairness—0
Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization—0
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness—0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domain—0
Interactive singing melody extraction based on active adaptation—0
Distilling Symbolic Priors for Concept Learning into Neural Networks—0
Progressive Conservative Adaptation for Evolving Target Domains—0
Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment—0
Learning mirror maps in policy mirror descent—0
More Flexible PAC-Bayesian Meta-Learning by Learning Learning AlgorithmsCode0
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—0
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning—0
Predicting Configuration Performance in Multiple Environments with Sequential Meta-learningCode0
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks—0
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks—0
CPT: Competence-progressive Training Strategy for Few-shot Node Classification—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